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A complete discussion on fully reconfigurable, digital, scalable, graph and sparsity-aware near-memory accelerator for graph neural networks

Siddhartha Raman Sundara Raman, LIZY JOHN, Jaydeep P. Kulkarni • ArXiv.org (2026)

Novel Applications (36.8%) Tools & Libraries (35.4%)

Advanced Machine Learning Enabled Modern Power System

Muhammad Asim Amin • CINECA IRIS Institutial Research Information System (University of Genoa) (2026)

Anomaly Detection & Fault Prediction (33.4%)

Machine Learning-Based Graph Simplification for Symbolic Accelerators

Tiffany Yu, Rye Stahle-Smith, Darssan Eswaramoorthi, Rasha Karakchi • ArXiv.org (2026)

Tools & Libraries (44.3%) Novel Applications (43.3%)

Multi-class anatomical landmark detection in periapical radiographs with deep learning

Cansu Buyuk, Alperen Saruhan, Fatma Yuce, Ozer Celik, Elif Bilgir, İbrahim Sevki Bayrakdar • Odontology (2026) • DOI: 10.1007/s10266-026-01409-0

Others

Comparative efficacy of supervised and semi-supervised machine learning classifiers for energy-based discrimination of near-fault pulse-type ground motion

Sayed S. R. Moustafa, M. Sami Soliman, Shimaa H. Elkhouly, Mohamed Yassien • Earth Science Informatics (2026) • DOI: 10.1007/s12145-026-02133-7

Anomaly Detection & Fault Prediction (37.3%)

Defect prediction for magnetorheological damper using a deep learning-based CLSTM classifier

Yoon-Mok Kim, Jong-Seok Oh, Bo-Gyu Kim • Journal of Mechanical Science and Technology (2026) • DOI: 10.1007/s12206-026-0410-y

Anomaly Detection & Fault Prediction (35.3%)

Automatic scheduling of satellite tracking tasks by means of policy-gradient reinforcement learning and transformer-based pointer networks

Ravi Dobariya, Thomas Hobiger • CEAS Space Journal (2026) • DOI: 10.1007/s12567-026-00726-y

Reinforcement Learning & Autonomous Systems (34.6%) Data Management (25.4%) Tools & Libraries (100.0%)

Integrated image processing and machine learning framework for defect detection and prediction in selective laser melting of Hastelloy X

M. Memari, A. Farzadi, H. Davarzani, A. Shamsipoor • Welding in the World (2026) • DOI: 10.1007/s40194-026-02476-1

Tools & Libraries (100.0%)

The diffusion-driven orthorhombic to tetragonal transition in YBa2Cu3O7 derived with a machine learning interatomic potential

Davide Gambino, Niccolò Di Eugenio, Jesper Byggmästar, Johan Klarbring, Daniele Torsello, Flyura Djurabekova, Francesco Laviano • npj Quantum Materials (2026) • DOI: 10.1038/s41535-026-00891-7

Tools & Libraries (100.0%)

Automated orbital wall fracture detection and severity classification of ocular injuries using deep learning

Farhad Salari, Zahra Meskar, Farhad Fallah, Maryam Fazeli, Reza Samiee, Hossein Arabalibeik, Seyed Mohsen Rafizadeh • Scientific Reports (2026) • DOI: 10.1038/s41598-026-54114-x

Others

Leveraging reinforcement learning, genetic algorithms and transformers for background determination in particle physics

Guillermo Hijano Mendizabal, Davide Lancierini, Alex Marshall, Andrea Mauri, Patrick Haworth Owen, Mitesh Patel, Konstantinos Petridis, Shah Rukh Qasim, Nicola Serra, William Sutcliffe, Hanae Tilquin • Physica Scripta (2026) • DOI: 10.1088/1402-4896/ae5c44

Novel Applications (42.4%) Reinforcement Learning & Autonomous Systems (42.2%)

Machine learning time propagators for time-dependent density functional theory simulations

Karan Shah, Attila Cangi • Machine Learning: Science and Technology (2026) • DOI: 10.1088/2632-2153/ae68d4

Novel Applications (37.3%) Reinforcement Learning & Autonomous Systems (33.3%)

A Multi-Task Deep Learning Model for Quality Control of Periapical Radiographs: Simultaneous Technical Error Classification and Quality Grading

Wen Fang, Leqi Liu, Xiaokan Wang, Jun Wang, Yuqing Meng, Keying Wu, Tie Liu, Bin Feng • Dentomaxillofacial Radiology (2026) • DOI: 10.1093/dmfr/twag021

Reviews (100.0%)

Quantum Phenomena

John N. Moye • Creating Learning Systems for Higher Education (2026) • DOI: 10.1108/978-1-80686-539-020261006

Reinforcement Learning & Autonomous Systems (29.7%)

A machine-learning-based approach for railway excitation force prediction

Hauke Schmidt, Gang Chen, Guozhen Jing, Daniel Zinken • Railway Sciences (2026) • DOI: 10.1108/rs-12-2025-0057

Anomaly Detection & Fault Prediction (26.3%) Tools & Libraries (100.0%)

Koopman-Operator-Based Control of Hypersonic Flight Vehicles With Few-Shot Learning for Internet of Aerospace Things

Wenjia Deng, Tingting Wang, Wencheng Zou, Jian Guo, Zhengrong Xiang • IEEE Internet of Things Journal (2026) • DOI: 10.1109/jiot.2026.3667989

Reinforcement Learning & Autonomous Systems (39.3%) Optimization & Control (32.4%)

Development of an Experimental Test Stand and Machine Learning Surrogate Models for Capturing Complex Responses in Pulsed Septa

Kawa, Krzysztof, Szumlak, Tomasz, Yagci, Omer Yusuf, Balhan, Bruno, Lackner, Friedrich • IEEE Trans.Appl.Supercond. (2026) • DOI: 10.1109/tasc.2026.3660099

Novel Applications (36.1%) Anomaly Detection & Fault Prediction (36.1%) Surrogate Models (100.0%)

Machine learning-driven inverse design of cosine beam honeycombs for zero Poisson's ratio and predetermined mechanical curves

Zi-Tao Wang, Hai-Tao Liu • International Journal of Applied Mechanics (2026) • DOI: 10.1142/s1758825126500535

Optimization & Control (32.9%)

Development of a deep-learning model for classification of intensivists' extubation decisions using ventilator graphic monitor images: a prospective observational study

Tatsuya Hayasaka, Masaki Nakane, Yuka Fujiwara, Yoko Yuzawa, Ryuto Yokoyama, Kiyotaka Soekawa, Masahiro Kuroki, Kenya Yarimizu, Yu Onodera, Tadanori Fukami, Hiroaki Toyama • Journal of Intensive Care (2026) • DOI: 10.1186/s40560-026-00889-0

Others

Reinforcement Learning Concepts

Jonas Hellgren, Johannes Lindgren • Reinforcement Learning Explained (2026) • DOI: 10.1201/9781003634409-3

Reinforcement Learning & Autonomous Systems (46.5%) Optimization & Control (27.5%)

Advanced Machine Learning Enabled Modern Power System

Muhammad Asim Amin • CINECA IRIS Institutial Research Information System (University of Genoa) (2026) • DOI: 10.15167/amin-muhammad-asim_phd2026-05-18

Anomaly Detection & Fault Prediction (33.4%)

Rapid Secondary Neutron Dose Estimation in Proton Accelerators Using FLUKA and Machine Learning

Demet Sariyer, Elif Yıldırım • Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi (2026) • DOI: 10.29233/sdufeffd.1783848

Statistics & Trends (44.7%) Novel Applications (44.6%)

How to Overcome Limitations of the Abaqus Superelasticity Material Model with Reduced Order Modeling and Machine Learning

M. Fassin, C. Degel, P. Hempel, K. Schulz • International Conference on Shape Memory and Superelastic Technologies (2026) • DOI: 10.31399/asm.cp.smst2026p0028

Statistics & Trends (25.4%) Beamline Design & Simulation (25.2%)

Navigating learning analytics: challenges, opportunities, and recommendations

Marte Blikstad-Balas • Handbook of Learning Analytics and Adaptive Learning in Schools (2026) • DOI: 10.4337/9781035330676.00026

Others

Machine Learning-Based Graph Simplification for Symbolic Accelerators

Tiffany Yu, Rye Stahle-Smith, Darssan Eswaramoorthi, Rasha Karakchi • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2605.08996

Tools & Libraries (44.3%) Novel Applications (43.3%)

A complete discussion on fully reconfigurable, digital, scalable, graph and sparsity-aware near-memory accelerator for graph neural networks

Siddhartha Raman Sundara Raman, LIZY JOHN, Jaydeep P. Kulkarni • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2605.19405

Novel Applications (36.8%) Tools & Libraries (35.4%)

A Sharper Picture of Generalization in Transformers

Paul Lintilhac, Sair Shaikh • arXiv (2026)

Anomaly Detection & Fault Prediction (26.6%)

Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC

Minghui Zhang, Xiaobin Li, Jie Chen, Ningtao Zhang, Fenhua Lu, Junrui Ma, Jiazhen Yan, Wanqin Tu, Xiaodong Tang, Bingshui Gao, Chengui Lu, Zhichao Zhang, Jinlong Zhang, Weiping Liu • arXiv (2026)

Novel Applications (30.3%) Tools & Libraries (27.2%)

📊 Publication Statistics & Trends

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Publication Trend Over Time

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A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans

Buzzi S, Mancosu P, Bresolin A, Gallo P, La Fauci F, Lobefalo F, Paganini L, Pelizzoli M, Reggiori G, Franzese C, Tomatis S, Scorsetti M, Lenardi C, Lambri N. • Bioengineering (Basel) (2025)

Anomaly Detection & Fault Prediction (27.4%) Statistics & Trends (25.3%)

A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL

Boukela, Lynda, Eichler, Annika, Branlard, Julien, Jomhari, Nur Zulaiha • arXiv (2024)

Anomaly Detection & Fault Prediction (43.4%) Novel Applications (36.9%)

Advanced Machine Learning Enabled Modern Power System

Muhammad Asim Amin • CINECA IRIS Institutial Research Information System (University of Genoa) (2026)

Anomaly Detection & Fault Prediction (33.4%)

AI-Enabled Operations at Fermi Complex: Multivariate Time Series Prediction for Outage Prediction and Diagnosis

Jain, Milan, Mutlu, Burcu O., Stam, Caleb, Strube, Jan, Schupbach, Brian A., John, Jason M.St., Pellico, William A. • arXiv (2025)

Anomaly Detection & Fault Prediction (45.2%) Tools & Libraries (100.0%)

Railway Wheel Impact Force and Alert Prediction Using Machine Learning Models

Gajendra Malviya, Shripad Salsingikar • AIRO Springer Series (2025) • DOI: 10.1007/978-3-031-90095-2_20

Anomaly Detection & Fault Prediction (32.4%)

Signal increase ratio prediction with CNNs

Marius Zeinhofer, Kent-Andre Mardal • Simula SpringerBriefs on Computing (2025) • DOI: 10.1007/978-3-032-00679-0_6

Anomaly Detection & Fault Prediction (26.9%)

Categories of Defect Prediction

Zhou Xu • Machine-Learning-Assisted Software Defect Prediction (2025) • DOI: 10.1007/978-3-032-01336-1_2

Anomaly Detection & Fault Prediction (43.2%)

Field-Reliability Predictions Based on Statistical System Lifecycle Models

Lukas Felsberger, Dieter Kranzlmüller, Benjamin Todd • 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE) (2018) • DOI: 10.1007/978-3-319-99740-7_7

Anomaly Detection & Fault Prediction (45.0%) Statistics & Trends (36.6%)

Predictive Modeling of ICU Mortality Using Supervised Machine Learning Algorithms

Malithi Upeksha, Sulanie Perera • Communications in Computer and Information Science (2025) • DOI: 10.1007/978-981-95-4409-7_11

Anomaly Detection & Fault Prediction (25.9%)

Preventive Maintenance of Mining Excavators Using Machine Learning: Enhancing Operational Efficiency and Cost-Effectiveness

Syed Adnan Ahmad, Prakash Kumar • Lecture Notes on Multidisciplinary Industrial Engineering (2025) • DOI: 10.1007/978-981-96-8058-0_15

Anomaly Detection & Fault Prediction (42.7%)

Steel Surface Defects Recognition Based on Multi-Type Statistical Features and Multiclass Support Vector Machine

Dibya Tripathi, Kheelraj Pandey, A. K. Chauhan • Lecture Notes on Multidisciplinary Industrial Engineering (2025) • DOI: 10.1007/978-981-96-8058-0_8

Anomaly Detection & Fault Prediction (34.7%)

Using Machine Learning to Predict First-Order Reaction Rate Constants of PFAS Degradation

Chenhao Pei, Yifan Qian, Jie Shen, Naichi Zhang, Qi Liu, Zhiqiang Li, Shuailei Pu, Tongliang Wu, Yujun Wang, Cun Liu • Bulletin of Environmental Contamination and Toxicology (2026) • DOI: 10.1007/s00128-025-04179-7

Anomaly Detection & Fault Prediction (33.2%)

Machine Learning-Based Prediction of Weld Pool Features in Laser-Arc Hybrid Welding of Ultra-High-Strength Wear-Resistant Steels

Bin Zhang, Zhe Gao, Sitong Li, Haijun Pan, Zhenguang Liu, Zigang Chen, Hongyan Liu, Xinjun Shen, Zhenxing Li, Xiaonan Wang • Journal of Materials Engineering and Performance (2026) • DOI: 10.1007/s11665-025-13120-2

Anomaly Detection & Fault Prediction (27.3%)

Comparative efficacy of supervised and semi-supervised machine learning classifiers for energy-based discrimination of near-fault pulse-type ground motion

Sayed S. R. Moustafa, M. Sami Soliman, Shimaa H. Elkhouly, Mohamed Yassien • Earth Science Informatics (2026) • DOI: 10.1007/s12145-026-02133-7

Anomaly Detection & Fault Prediction (37.3%)

Defect prediction for magnetorheological damper using a deep learning-based CLSTM classifier

Yoon-Mok Kim, Jong-Seok Oh, Bo-Gyu Kim • Journal of Mechanical Science and Technology (2026) • DOI: 10.1007/s12206-026-0410-y

Anomaly Detection & Fault Prediction (35.3%)

Advanced quality assessment and damage monitoring in 3D-printed plastic and composite beams using machine learning

Gia Hoang Phan • Progress in Additive Manufacturing (2026) • DOI: 10.1007/s40964-025-01514-6

Anomaly Detection & Fault Prediction (30.6%) Beam Diagnostics (25.9%)

Toward closed-loop quality assurance in powder bed fusion additive manufacturing: Defect detection, machine learning, and computational modeling

Mohammad Taghian, Ali Pilehvar Meibody, Abdollah Saboori, Luca Iuliano • Journal of Manufacturing Processes (2026) • DOI: 10.1016/j.jmapro.2026.01.026

Anomaly Detection & Fault Prediction (25.8%)

A physics-informed machine learning framework for unified prediction of superconducting transition temperatures

Ehsan Alibagheri, Mohammad Sandoghchi, Alireza Seyfi, Mohammad Khazaei, S. Mehdi Vaez Allaei • Materials Today Physics (2026) • DOI: 10.1016/j.mtphys.2025.101971

Anomaly Detection & Fault Prediction (28.2%) Statistics & Trends (25.8%) Tools & Libraries (100.0%)

Integrated engineering framework for fatigue damage prediction of fighter aircraft using machine learning

Zahid Ahmad, Ghulam Murtaza Amjad, Mian Ashfaq Ali, Faisal Siddiqui, Aamir Mubashar, Muhammad Ahsan • Results in Engineering (2025) • DOI: 10.1016/j.rineng.2025.107713

Anomaly Detection & Fault Prediction (41.7%) Tools & Libraries (100.0%)

Thermal Runaway of Chemical Reactors: An Experimental, Modeling, and Machine-Learning Investigation

Tobias K. Misicko, Joaquin Herrero, Jacob C. Robinson, Daniela S. Mainardi, Yang Xiao • Industrial & Engineering Chemistry Research (2026) • DOI: 10.1021/acs.iecr.5c04085

Anomaly Detection & Fault Prediction (27.2%) Statistics & Trends (26.6%)

Assessment of machine learning models for predicting the deflection of concrete beams

Arvindan Sivasuriyan, Dhanasingh Sivalinga Vijayan, Roman Trach, Hydayatullah Bayat, Artur Góral, Anna Piętocha, Eugeniusz Koda • Scientific Reports (2026) • DOI: 10.1038/s41598-026-47819-6

Anomaly Detection & Fault Prediction (26.1%)

Particle peak velocity prediction based on risk-oriented hybrid ensemble learning

Lijie Ge, Jianhui He, Zhuang Zhang, Lujun Yin, Shiqi Jia, Yan Zhao • Scientific Reports (2026) • DOI: 10.1038/s41598-026-48622-z

Anomaly Detection & Fault Prediction (29.5%) Statistics & Trends (27.5%)

A physics-guided temporal convolutional learning with XGBoost integration for degradation-aware and robust electric vehicle range prediction

Kaliappan Esakkiappan, P. Gowtham, V. Beula Shanthi Ammani Ammal, D. Shalini, T. Mariprasath, Seif Al Bustanji, Viktoriia Bereznychenko • Scientific Reports (2026) • DOI: 10.1038/s41598-026-49119-5

Anomaly Detection & Fault Prediction (29.9%) Reinforcement Learning & Autonomous Systems (28.0%)

An advanced pulse-avalanche stochastic model of long gamma-ray burst light curves

Maistrello, Manuele, Ferro, Lisa, Bazzanini, Lorenzo, Maccary, Romain, Guidorzi, Cristiano • Astron.Astrophys. (2025) • DOI: 10.1051/0004-6361/202553821

Anomaly Detection & Fault Prediction (32.9%) Statistics & Trends (31.8%) Tools & Libraries (100.0%)

Machine learning for mapping multipactor susceptibility in RF systems: Capabilities and generalization constraints

Asif Iqbal, John Verboncoeur, Peng Zhang • APL Machine Learning (2026) • DOI: 10.1063/5.0333133

Anomaly Detection & Fault Prediction (33.4%) RF Systems (28.8%)

Vibrational characterization and machine learning-based classification of defects in TWCNTs

Jalpa Ardeshana, Bhavik Ardeshana, Umang Jani, Jay Vala, Ajay Patel, Hitesh Shah, Vimal Vaghela • Mechanics Based Design of Structures and Machines (2026) • DOI: 10.1080/15397734.2026.2614981

Anomaly Detection & Fault Prediction (29.8%) Beam Diagnostics (25.2%)

IGD-YOLOv8s: insulator defect detection via iterative attention and generalized dynamic feature pyramids

Zhiqin Zhang, Guowu Yuan, Junlin Rao, Xiang Li, Pengfei Yu, Hao Zhou • Measurement Science and Technology (2025) • DOI: 10.1088/1361-6501/ae1858

Anomaly Detection & Fault Prediction (37.8%) Data Management (25.5%)

Research on mechanical equipment defect detection based on optimized deep learning model with multiple attention mechanism

Longkai Liu, Xinru Wei, Shan Wang, Na Wang, Pingjuan Niu • Engineering Research Express (2026) • DOI: 10.1088/2631-8695/ae62cf

Anomaly Detection & Fault Prediction (39.0%) Tools & Libraries (29.4%)

Efficient Two-Stage Sequential Machine Learning Model for Progressive Damage Analysis in Fiber-Reinforced Composites

Mohammadamin Alibakhshi, Parisa Khodabakhshi • Machine Learning: Engineering (2026) • DOI: 10.1088/3049-4761/ae565d

Anomaly Detection & Fault Prediction (30.0%)

Optimizing request for information (RFI) prioritization with machine learning

• Development and Learning in Organizations: An International Journal (2025) • DOI: 10.1108/dlo-12-2025-0441

Anomaly Detection & Fault Prediction (25.9%) Reviews (100.0%) Tools & Libraries (100.0%)

Advanced displacement prediction of deep roadway roof with data fusion using machine-learning models: a comparative study

Yan Dong, Jun Zheng, Jiang Xu, Kai Wang, Yi Li • Engineering Computations (2026) • DOI: 10.1108/ec-07-2025-0843

Anomaly Detection & Fault Prediction (30.3%) Tools & Libraries (100.0%)

Presenting new approaches based on Zhao–Atlas–Marks distribution and machine learning for detecting structural damage in steel beams

Hamid Reza Ahmadi, Hesam Hooshyar, Mahmoud Bayat • International Journal of Structural Integrity (2025) • DOI: 10.1108/ijsi-04-2025-0088

Anomaly Detection & Fault Prediction (33.4%) Beam Diagnostics (32.4%)

A machine-learning-based approach for railway excitation force prediction

Hauke Schmidt, Gang Chen, Guozhen Jing, Daniel Zinken • Railway Sciences (2026) • DOI: 10.1108/rs-12-2025-0057

Anomaly Detection & Fault Prediction (26.3%) Tools & Libraries (100.0%)

Enhancing RFI management in construction through machine learning-driven predictive models

Neziha Yilmaz, Esin Ergen, Ahmet Muhtar Citipitioglu • Smart and Sustainable Built Environment (2025) • DOI: 10.1108/sasbe-05-2025-0254

Anomaly Detection & Fault Prediction (28.5%) Tools & Libraries (100.0%)

Identification of Lightning Strikes to Towers Using Their Electric Field Signatures and a Machine Learning Approach

Ziqin Ding, Si Chen, Hanbo Yang, Kang Yang, Vladimir A. Rakov, Joel B. Harley, Yanan Zhu, Istvan Kereszy • IEEE Sensors Journal (2026) • DOI: 10.1109/jsen.2025.3637548

Anomaly Detection & Fault Prediction (30.8%)

Robust Leakage Localization in Radioactive Material Transport via Multitask Learning

Yafei Chen, Shaohua Hu, Kaijing Yang, Hualuo Sheng, Chaofeng Wang, Yaping Wan • IEEE Sensors Journal (2026) • DOI: 10.1109/jsen.2025.3639432

Anomaly Detection & Fault Prediction (29.5%)

Development of an Experimental Test Stand and Machine Learning Surrogate Models for Capturing Complex Responses in Pulsed Septa

Kawa, Krzysztof, Szumlak, Tomasz, Yagci, Omer Yusuf, Balhan, Bruno, Lackner, Friedrich • IEEE Trans.Appl.Supercond. (2026) • DOI: 10.1109/tasc.2026.3660099

Novel Applications (36.1%) Anomaly Detection & Fault Prediction (36.1%) Surrogate Models (100.0%)

A PSO-random forest-based cross-domain fault pattern recognition system for high-speed train bearings

Junjie Lin, Zhuofu Deng • International Conference on Machine Vision, Intelligent Imaging, and Pattern Recognition (MVIIPR 2025) (2026) • DOI: 10.1117/12.3110524

Anomaly Detection & Fault Prediction (39.1%)

An Overview of Artificial Intelligence and Machine Learning

Adwika Singh, Falguni Sharma, Anshuman Shastri • Artificial Intelligence for Biomass-based Biofuel Production (2025) • DOI: 10.1201/9781003571223-4

Anomaly Detection & Fault Prediction (33.8%) Reinforcement Learning & Autonomous Systems (30.8%)

Support Vector Machines

Yinglin Xia, Jun Sun • Machine Learning for Microbiome Statistics (2026) • DOI: 10.1201/9781003610281-8

Anomaly Detection & Fault Prediction (31.9%) Tools & Libraries (25.4%)

ESTIMATIVA DE VIDA ÚTIL REMANESCENTE DE PLACAS CERÂMICAS UTILIZADAS EM FILTRAGEM DE MINÉRIO DE FERRO ATRAVÉS DE MODELO DE CONFIABILIDADE E MÉTODOS DE MACHINE LEARNING APLICADOS A VARIÁVEIS DE PROCESSO INDUSTRIAL DE UM SISTEMA PIMS

ROBERT BENTO FLORENTINO, LUIZ GUSTAVO LOURENÇO MOURA • Anais do Encontro Nacional de Engenharia de Produção (2025) • DOI: 10.14488/enegep2025_tn_wpg_424_2075_48651

Anomaly Detection & Fault Prediction (28.1%)

Advanced Machine Learning Enabled Modern Power System

Muhammad Asim Amin • CINECA IRIS Institutial Research Information System (University of Genoa) (2026) • DOI: 10.15167/amin-muhammad-asim_phd2026-05-18

Anomaly Detection & Fault Prediction (33.4%)

Study on estimation of internal cracks using electromagnetic waves and machine learning

Hinata Yamamoto, Hitoshi Ito, Toshiaki Mizobuchi • Proceedings of the Institution of Civil Engineers - Construction Materials (2025) • DOI: 10.1680/jcoma.24.00099

Anomaly Detection & Fault Prediction (31.9%) Novel Applications (31.0%)

Deep learning framework for fault detection in accelerators

Piekarski, Michal • JACoW (2024) • DOI: 10.18429/jacow-ibic2024-wep47

Beam Diagnostics (43.4%) Anomaly Detection & Fault Prediction (41.9%) Tools & Libraries (100.0%)

Machine learning using beam loss monitors for DIAMOND-II

Lehmann, Corey, Bobb, Lorraine, Burrows, Philip • JACoW (2026) • DOI: 10.18429/jacow-ibic2025-mopco04

Anomaly Detection & Fault Prediction (47.3%) Beam Diagnostics (43.1%)

Machine learning for calibration drift forecasting in superconducting RF cavities

Sun Yue, Bellandi Andrea, Eichler, Annika, Richter,Bozo, Diomede,Marco, Schmidt, Christian, Schlarb Holger, Branlard, Julien, Herrmann,Max • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thps135

RF Systems (49.2%) Anomaly Detection & Fault Prediction (34.5%)

Anomaly detection of slow-moving variables at LANSCE for improved beam quality

Huang, En-Chuan, Yampolsky, Nikolai • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop007

Anomaly Detection & Fault Prediction (52.2%) Beam Diagnostics (44.5%)

Machine learning-enhanced infrared imaging for temperature anomaly detection in power supplies

Mohsen, Osama, Lobach, Ihar, Borland, Michael, Sun, Yine • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop059

Anomaly Detection & Fault Prediction (45.9%) Novel Applications (37.0%)

Multiple-Class Classification of a Diesel Locomotive Engine’s Mechanical Condition Based on Wavelet Properties of Vibration Signals via Machine-Learning Techniques

Andrey Belyaev • Bulletin of scientific research results (2026) • DOI: 10.20295/2223-9987-2026-1-142-153

Anomaly Detection & Fault Prediction (28.8%)

ReliaLearnR: Learning Modules for Reliability Analysis

Paul B. Govan • Journal of Open Source Education (2026) • DOI: 10.21105/jose.00302

Anomaly Detection & Fault Prediction (30.9%)

The Research on Chain Fault Prediction in AC-DC Hybrid Power Grids Based on Deep Learning

Wenchao Qin, Xufei Liu, Yiran Tao • Springer Science and Business Media LLC (2026) • DOI: 10.21203/rs.3.rs-8721157/v1

Anomaly Detection & Fault Prediction (39.1%)

Development of a Machine Vision Based Non-Contact Metrology System for Inspection of Mechanical Components

Prathmesh Shinde • International Journal for Research in Applied Science and Engineering Technology (2026) • DOI: 10.22214/ijraset.2026.80244

Anomaly Detection & Fault Prediction (34.4%) Tools & Libraries (26.0%)

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

Hal Ferguson • ODU Digital Commons (Old Dominion University) (2026) • DOI: 10.25777/2pjk-8q69

Anomaly Detection & Fault Prediction (46.1%)

Predictive maintenance of induction motors using machine learning: A random forest based fault prediction approach

Swati Sucharita Barik, Anjali Gaur, Amruta Kulkarni, Rasika Pardeshi, Saniya Satpute • World Journal of Advanced Research and Reviews (2026) • DOI: 10.30574/wjarr.2026.30.1.0757

Anomaly Detection & Fault Prediction (60.0%) Reviews (100.0%)

DEEP LEARNING-BASED CLASSIFICATION OF SEISMIC DAMAGE ON PARTITION WALL AND ARTIFICIAL CRACKING PATTERN

Kotoka FUJISAWA, Noriyuki TAKAHASHI • AIJ Journal of Technology and Design (2025) • DOI: 10.3130/aijt.31.1391

Anomaly Detection & Fault Prediction (32.2%)

Machine Learning in Semiconductor Failure Analysis: Techniques and Case Studies (2025 Update)

Michael Kögel, Sebastian Brand, Frank Altmann • International Symposium for Testing and Failure Analysis (2025) • DOI: 10.31399/asm.cp.istfa2025tp00x1

Anomaly Detection & Fault Prediction (51.1%) Statistics & Trends (26.3%)

Guided waves with machine learning for structural health monitoring: transparent features and Monte Carlo confidence

Brazalez Juan, Nabarrete Airton • Fracture and Structural Integrity (2026) • DOI: 10.3221/igf-esis.76.02

Anomaly Detection & Fault Prediction (35.0%) Beam Diagnostics (27.7%)

Support Vector Machine and k-Means Clustering for Advanced Wheel Flat Identification: A Comparison of Supervised and Unsupervised Methods

Alireza Chegini, Mohammadreza Mohammadi, Araliya Mosleh, Cecilia Vale, Ramin Ghiasi, Ruben Silva, Antonio Guedes, Andreia Meixedo, Abdollah Malekjafarian • Machines (2026) • DOI: 10.3390/machines14030286

Anomaly Detection & Fault Prediction (29.5%) Tools & Libraries (100.0%)

A Physics-informed Deep Learning Model for the Magnetic Classification of Solar Active Regions

Ming Li, Siwei Liu, Jingjing Wang, Yanmei Cui, Bingxian Luo, Xin Wang • The Astrophysical Journal Supplement Series (2026) • DOI: 10.3847/1538-4365/ae4595

Anomaly Detection & Fault Prediction (27.7%)

Sintering Process Analysis of Aluminum Matrix Composites Using Machine Learning

Kenjiro Sugio, Yuuki Shinohara, Yoshikazu Hayashi, Gen Sasaki • Defect and Diffusion Forum (2026) • DOI: 10.4028/p-a6ba7w

Anomaly Detection & Fault Prediction (28.3%)

Diagnosing Induction Motor Fault Based on ReliefF Feature Selection Algorithm and Support Vector Machine Model

Felly Anta, Didik Djoko Susilo, Zainal Arifin • Applied Mechanics and Materials (2026) • DOI: 10.4028/p-me4dzs

Anomaly Detection & Fault Prediction (39.5%)

Fault Prediction of 5G Base Station RF Module Based on Machine Learning Algorithms

Mingzan Ning • Applied and Computational Engineering (2025) • DOI: 10.54254/2755-2721/2025.28592

Anomaly Detection & Fault Prediction (38.8%) Tools & Libraries (100.0%)

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń, Ernesto De Matteis • arXiv (2017)

Novel Applications (41.1%) Anomaly Detection & Fault Prediction (38.2%)

Radar Emitter Classification with Attribute-specific Recurrent Neural Networks

Paolo Notaro, Magdalini Paschali, Carsten Hopke, David Wittmann, Nassir Navab • arXiv (2019)

Anomaly Detection & Fault Prediction (27.2%) Tools & Libraries (27.1%)

Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator

Willem Blokland, Pradeep Ramuhalli, Charles Peters, Yigit Yucesan, Alexander Zhukov, Malachi Schram, Kishansingh Rajput, Torri Jeske • arXiv (2021)

Novel Applications (46.8%) Anomaly Detection & Fault Prediction (45.1%)

Anomaly Detection in Particle Accelerators using Autoencoders

Jonathan P. Edelen, Nathan M. Cook • arXiv (2021)

Anomaly Detection & Fault Prediction (41.9%) Novel Applications (41.6%) Tools & Libraries (100.0%)

Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning

Majdi I. Radaideh, Chris Pappas, Mark Wezensky, Pradeep Ramuhalli, Sarah Cousineau • arXiv (2022)

Anomaly Detection & Fault Prediction (41.9%) Novel Applications (31.9%)

Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression

Sichen Li, Jochem Snuverink, Fernando Perez-Cruz, Andreas Adelmann • arXiv (2023)

Statistics & Trends (41.5%) Anomaly Detection & Fault Prediction (41.3%)

Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source

Ryan Humble, William Colocho, Finn O'Shea, Daniel Ratner, Eric Darve • arXiv (2023)

Anomaly Detection & Fault Prediction (39.3%) Novel Applications (31.8%)

Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators

Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta • arXiv (2024)

Anomaly Detection & Fault Prediction (48.8%) Novel Applications (37.5%)

Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology

Zhiyuan Chen, Wei Lu, Radhika Bhong, Yimin Hu, Brian Freeman, Adam Carpenter • arXiv (2024)

Anomaly Detection & Fault Prediction (46.1%) Novel Applications (42.2%)

Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory

Monibor Rahman, Adam Carpenter, Khan Iftekharuddin, Chris Tennant • arXiv (2024)

Anomaly Detection & Fault Prediction (40.5%) Data Management (32.9%)

Automated Anomaly Detection on European XFEL Klystrons

Antonin Sulc, Annika Eichler, Tim Wilksen • arXiv (2024)

Anomaly Detection & Fault Prediction (47.9%) RF Systems (42.5%)

A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL

Lynda Boukela, Annika Eichler, Julien Branlard, Nur Zulaiha Jomhari • arXiv (2024)

Anomaly Detection & Fault Prediction (43.4%) Novel Applications (36.9%)

AI-Enabled Operations at Fermi Complex: Multivariate Time Series Prediction for Outage Prediction and Diagnosis

Milan Jain, Burcu O. Mutlu, Caleb Stam, Jan Strube, Brian A. Schupbach, Jason M. St. John, William A. Pellico • arXiv (2025)

Anomaly Detection & Fault Prediction (45.2%) Tools & Libraries (100.0%)

Radar Pulse Deinterleaving with Transformer Based Deep Metric Learning

Edward Gunn, Adam Hosford, Daniel Mannion, Jarrod Williams, Varun Chhabra, Victoria Nockles • arXiv (2025)

Tools & Libraries (28.4%) Anomaly Detection & Fault Prediction (28.2%)

Coincident Learning for Beam-based RF Station Fault Identification Using Phase Information at the SLAC Linac Coherent Light Source

Jia Liang, William Colocho, Franz-Josef Decker, Ryan Humble, Ben Morris, Finn H. O'Shea, David A. Steele, Zhe Zhang, Eric Darve, Daniel Ratner • arXiv (2025)

Anomaly Detection & Fault Prediction (45.0%) Beam Diagnostics (38.1%) Tools & Libraries (100.0%)

SABER: Symbolic Regression-based Angle of Arrival and Beam Pattern Estimator

Shih-Kai Chou, Mengran Zhao, Cheng-Nan Hu, Kuang-Chung Chou, Carolina Fortuna, Jernej Hribar • arXiv (2025)

Beam Diagnostics (35.0%) Anomaly Detection & Fault Prediction (28.3%) Tools & Libraries (100.0%)

Multimodal Learning for Arcing Detection in Pantograph-Catenary Systems

Hao Dong, Eleni Chatzi, Olga Fink • arXiv (2026)

Anomaly Detection & Fault Prediction (27.5%) Tools & Libraries (100.0%)

A Sharper Picture of Generalization in Transformers

Paul Lintilhac, Sair Shaikh • arXiv (2026)

Anomaly Detection & Fault Prediction (26.6%)

RBF neural net based classifier for the AIRIX accelerator fault diagnosis

J. C. Ribes, G. Delaunay, J. Delvaux, E. Merle, M. Mouillet • arXiv (2000)

Anomaly Detection & Fault Prediction (42.2%) Novel Applications (34.5%) Tools & Libraries (100.0%)

Control

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RL-ABC: Reinforcement Learning for Accelerator Beamline Control

Ibrahim, Anwar, Ratnikov, Fedor, Kaledin, Maxim, Petrenko, Alexey, Derkach, Denis • arXiv (2026)

Reinforcement Learning & Autonomous Systems (53.2%) Operations & Control (43.6%) Tools & Libraries (100.0%)

Optimized Supervised Machine Learning for Accurate Estimation of Reinforcement in RC Beams and Columns

Nhan Thanh Vu Nguyen, Chon Tran, Duong Thai Le, Quy Thue Nguyen • Lecture Notes in Civil Engineering (2025) • DOI: 10.1007/978-3-032-04645-1_20

Reinforcement Learning & Autonomous Systems (41.1%) Optimization & Control (34.2%)

Measurement and Evaluation of Intelligent Robots

Claude Sammut • Lecture Notes in Computer Science (2025) • DOI: 10.1007/978-3-032-09087-4_13

Reinforcement Learning & Autonomous Systems (31.9%) Operations & Control (30.9%)

Optimization of the Claus Process Using Machine Learning and Real Operational Data: A Case Study in an SRU

Amirfarzam Shokouhalaei • Chemistry Africa (2026) • DOI: 10.1007/s42250-025-01560-1

Optimization & Control (31.3%) Reinforcement Learning & Autonomous Systems (30.8%)

Optimizing pulsed field ablation for cardiac arrhythmias integrating Taguchi method, machine learning and genetic algorithms

Ethan Nabuurs, Kuljeet Singh Grewal, Amara Sanchez, Mikkayla Ellsworth-Reid, Robert F. Gilmour, Roderick Melnik, Sundeep Singh • Results in Engineering (2026) • DOI: 10.1016/j.rineng.2026.108978

Optimization & Control (28.0%)

Microsecond-latency feedback at a particle accelerator by online reinforcement learning on hardware

L. Scomparin, Michele Caselle, Andrea Santamaria Garcia, Chenran Xu, Edmund Blomley, Timo Dritschler, Akira Mochihashi, Marcel Schuh, Johannes Leonhard Steinmann, Erik Bründermann, Andreas Kopmann, Jürgen Becker, A.-S. Mueller, Marc Weber • Machine Learning Science and Technology (2026) • DOI: 10.1088/2632-2153/ae5b20

Reinforcement Learning & Autonomous Systems (60.1%) Operations & Control (55.2%)

Batch spacing optimization by reinforcement learning

Remta, Matthias, Velotti, Francesco, Rezagholi, Sharwin • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/g9wr-197z

Reinforcement Learning & Autonomous Systems (61.3%) Operations & Control (52.5%)

Application of ensemble machine learning algorithms and filtering techniques in slow orbit feedback systems of electron storage rings

Fan, Jiaqi, Liu, Weibin, Wang, Jiuqing, Wei, Yanru, Wei, Yuanyuan, Ji, Daheng • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/physrevaccelbeams.28.042801

Optimization & Control (36.3%) Reinforcement Learning & Autonomous Systems (35.4%) Tools & Libraries (100.0%)

TTBO-FL: Joint Training, Trajectory, and Beamforming Optimization for Energy-Efficient Federated Learning in UAV Swarm

Chunyu Zhang, Yanlu Li, Yiming Liu, Yuzhen Huang, Zhi Zhang • IEEE Internet of Things Journal (2026) • DOI: 10.1109/jiot.2025.3628900

Reinforcement Learning & Autonomous Systems (35.4%) Optimization & Control (28.8%)

Koopman-Operator-Based Control of Hypersonic Flight Vehicles With Few-Shot Learning for Internet of Aerospace Things

Wenjia Deng, Tingting Wang, Wencheng Zou, Jian Guo, Zhengrong Xiang • IEEE Internet of Things Journal (2026) • DOI: 10.1109/jiot.2026.3667989

Reinforcement Learning & Autonomous Systems (39.3%) Optimization & Control (32.4%)

Reinforcement learning for accelerator beamline control: A simulation-based approach

Anwar Ibrahim, A. Petrenko, Maxim Kaledin, Ehab S Suleiman, F. Ratnikov, Д. Деркач • International Journal of Modern Physics E (2026) • DOI: 10.1142/s0218301326410272

Reinforcement Learning & Autonomous Systems (64.1%) Operations & Control (58.8%)

Machine learning-driven inverse design of cosine beam honeycombs for zero Poisson's ratio and predetermined mechanical curves

Zi-Tao Wang, Hai-Tao Liu • International Journal of Applied Mechanics (2026) • DOI: 10.1142/s1758825126500535

Optimization & Control (32.9%)

A Teachable Machine for Transformers

Nicolas Pope, Matti Tedre • Proceedings of the 25th Koli Calling International Conference on Computing Education Research (2025) • DOI: 10.1145/3769994.3770061

Novel Applications (30.4%) Operations & Control (28.0%)

Reinforcement Learning Concepts

Jonas Hellgren, Johannes Lindgren • Reinforcement Learning Explained (2026) • DOI: 10.1201/9781003634409-3

Reinforcement Learning & Autonomous Systems (46.5%) Optimization & Control (27.5%)

Integrating Industry 5.0 principles into electrical machine design

Pankaj Kumar Keshri • Artificial Intelligence-Powered Electrical Machine Design (2026) • DOI: 10.1201/9781003641582-12

Operations & Control (31.3%) Statistics & Trends (28.5%)

Machine, Deep, and Reinforcement Learning Algorithms and Applications

K. Gowri, B. L. Shivakumar • Real-Time Artificial Intelligence (AI) (2025) • DOI: 10.1201/9781998511358-3

Reinforcement Learning & Autonomous Systems (50.0%) Optimization & Control (36.3%)

Comparative evaluation of machine learning models for thrust force estimation in aluminum 5083 H111

Emre Teke, Neslihan Özsoy, Deniz Demircioğlu Diren, Murat Özsoy • Materials Testing (2026) • DOI: 10.1515/mt-2025-0389

Statistics & Trends (26.1%) Optimization & Control (25.4%) Tools & Libraries (100.0%)

Continuous data-driven control of the GTS-LHC ion source at CERN

Kain, Verena, Rodriguez Mateos, Borja, Bruchon, Niky, Hirlaender, Simon, Küchler, Detlef • JACoW (2024) • DOI: 10.18429/jacow-ecris2024-mop11

Data Management (53.3%) Operations & Control (53.0%)

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Roussel, Ryan, Kennedy,Dylan, Edelen, Auralee, Kuklev, Nikita, Miskovich,Sara, Zhang Zhe • JACOW (2025) • DOI: 10.18429/jacow-icalepcs2025-wesv001

Reinforcement Learning & Autonomous Systems (59.9%) Operations & Control (55.7%)

Uncertainty-Quantified Machine Model Construction Using Physics-Informed Gaussian Processes and Bayesian Optimization

Isensee,Victoria, Oeftiger, Adrian, Boine-Frankenheim, Oliver • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thpm017

Statistics & Trends (28.9%) Optimization & Control (28.8%)

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

Whitbeck, A., Berlioz, J., Danison-Fieldhouse, K., Hazelwood, K., Khan, M., Mitrevski, J., Narayanan, A., John, J.St., Tran, N., Ji, J., Walter, M. • Journal (2025) • DOI: 10.18429/jacow-napac2025-mop039

Novel Applications (37.3%) Optimization & Control (36.1%)

Hybrid Machine Learning based Optimization with Beamforming of 6G Mobile Radio Network for LEO Satellite base station in Urban Areas

Haymanot Derebe Bizuneh • Springer Science and Business Media LLC (2025) • DOI: 10.21203/rs.3.rs-8089868/v1

Optimization & Control (31.9%)

Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach

Ibrahim Anwar, Petrenko, Alexey, Kaledin, Maxim, Suleiman, Ehab, Ratnikov, Fedor, Derkach Denis • arXiv (Cornell University) (2025) • DOI: 10.48550/arxiv.2510.26805

Reinforcement Learning & Autonomous Systems (64.1%) Operations & Control (58.8%)

First Experimental Demonstration of Machine Learning-Based Tuning on the PSI Injector 2 Cyclotron

Sandrine Marquié • arXiv (Cornell University) (2025) • DOI: 10.48550/arxiv.2512.03829

Novel Applications (40.8%) Optimization & Control (39.6%)

“War Machine” 2026 Srteaming-Ita CB01 [Altadefinizione]

coeth mitch • ResearchHub Technologies, Inc. (2026) • DOI: 10.55277/researchhub.tvj62yy9

Operations & Control (26.0%)

Neural Networks for Modeling and Control of Particle Accelerators

A. L. Edelen, S. G. Biedron, B. E. Chase, D. Edstrom, S. V. Milton, P. Stabile • arXiv (2016)

Novel Applications (53.9%) Operations & Control (53.8%)

Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities

Jorge Alberto Diaz Cruz, Sandra Biedron, Manel Martinez-Ramon, Salvador Sosa, Reza Pirayesh • arXiv (2019)

Optimization & Control (40.3%) RF Systems (36.9%) Tools & Libraries (100.0%)

Machine learning for design optimization of storage ring nonlinear dynamics

Faya Wang, Minghao Song, Auralee Edelen, Xiaobiao Huang • arXiv (2019)

Optimization & Control (43.7%) Novel Applications (31.2%) Surrogate Models (100.0%)

Online tuning and light source control using a physics-informed Gaussian process Adi

A. Hanuka, J. Duris, J. Shtalenkova, D. Kennedy, A. Edelen, D. Ratner, X. Huang • arXiv (2019)

Optimization & Control (46.2%) Operations & Control (42.3%)

TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning

Francesco Capuano, Davorin Peceli, Gabriele Tiboni, Raffaello Camoriano, Bedřich Rus • arXiv (2023)

Reinforcement Learning & Autonomous Systems (45.2%) Optimization & Control (35.9%) Tools & Libraries (100.0%)

Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia, Oliver Stein, Erik Bründermann, Willi Kuropka, Hannes Dinter, Frank Mayet, Thomas Vinatier, Florian Burkart, Holger Schlarb • arXiv (2023)

Reinforcement Learning & Autonomous Systems (57.9%) Optimization & Control (46.4%)

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

A. Whitbeck, J. Berlioz, K. Danison-Fieldhouse, K. Hazelwood, M. Khan, J. Mitrevski, A. Narayanan, J. St. John, N. Tran, J. Ji, M. Walter • arXiv (2025)

Novel Applications (37.3%) Optimization & Control (36.1%)

RL-ABC: Reinforcement Learning for Accelerator Beamline Control

Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin, Alexey Petrenko, Denis Derkach • arXiv (2026)

Reinforcement Learning & Autonomous Systems (53.2%) Operations & Control (43.6%) Tools & Libraries (100.0%)

Novel Applications

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A complete discussion on fully reconfigurable, digital, scalable, graph and sparsity-aware near-memory accelerator for graph neural networks

Siddhartha Raman Sundara Raman, LIZY JOHN, Jaydeep P. Kulkarni • ArXiv.org (2026)

Novel Applications (36.8%) Tools & Libraries (35.4%)

A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE

Mishra, Aashwin, Seaberg, Matt, Roussel, Ryan, Poitevin, Fred, Thayer, Jana, Ratner, Daniel, Edelen, Auralee, Mehta, Apurva • arXiv (2025)

Novel Applications (45.5%) Tools & Libraries (41.7%)

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry

Iqbal, Asif, Verboncoeur, John, Zhang, Peng • arXiv (2025)

Novel Applications (38.9%) Statistics & Trends (38.6%) Tools & Libraries (100.0%)

A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL

Boukela, Lynda, Eichler, Annika, Branlard, Julien, Jomhari, Nur Zulaiha • arXiv (2024)

Anomaly Detection & Fault Prediction (43.4%) Novel Applications (36.9%)

Analysis note: measurement of thrust in $e^{+}e^{-}$ collisions at $\sqrt{s}$ = 91 GeV with archived ALEPH data

Badea, Anthony, Baty, Austin, Bossi, Hannah, Chen, Yu-Chen, Chen, Yi, Zhang, Jingyu, Innocenti, Gian Michele, Maggi, Marcello, McGinn, Chris, Peters, Michael, Sheng, Tzu-An, Mikuni, Vinicius, Avaylon, Matthew, Komiske, Patrick, Metodiev, Eric, Thaler, Jesse, Nachman, Benjamin, Lee, Yen-Jie • arXiv (2025)

Novel Applications (40.0%) Statistics & Trends (37.4%)

Application of quantum machine learning using variational quantum classifier in accelerator physics

Yin, He-Xing, Hu, Zhi-Yuan, Zeng, Huan-Huan, Guan, Jia-Bao, Wang, Ji-ke • arXiv (2025)

Novel Applications (41.9%) Statistics & Trends (34.1%)

Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models

Rodriguez-Llorente, Guillermo, Gallardo, Galo, Navascués, Rodrigo Morant, Petrovsky, Nikita Khvatkin, Sabogal, Anderson, Martín, Roberto Gómez-Espinosa • Journal (2025)

Novel Applications (46.0%) Reinforcement Learning & Autonomous Systems (45.9%) Surrogate Models (100.0%)

Beamline Steering Using Deep Learning Models

Allen, Dexter, Kante, Isaac, Bohler, Dorian • arXiv (2024)

Novel Applications (48.8%) Tools & Libraries (44.4%)

Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments

Linus Pithan, Vladimir Starostin, David Mareček, Lukas Petersdorf, Constantin Völter, Valentin Munteanu, Maciej Jankowski, Oleg Konovalov, Alexander Gerlach, Alexander Hinderhofer, Bridget Murphy, Stefan Kowarik, Frank Schreiber • HAL (2023)

Novel Applications (36.4%) Tools & Libraries (100.0%)

Data-Driven Discovery of Beam Centroid Dynamics

Pocher, Liam A., Haber, Irving, Antonsen, Thomas M., O'Shea, Patrick G. • arXiv (2024)

Novel Applications (48.3%) Beamline Design & Simulation (45.5%)

Developing Machine Learning Models for Proton Computed Tomography and LHCb Particle Tracking

Ackernley, Thomas • Thesis (2025)

Novel Applications (46.3%) Tools & Libraries (42.2%)

Digital Twin Framework for PIP-II Linac: AI-Driven Multi-Scale Modeling from Ion Source to 800 MeV

Pathak, Abhishek, Hanlet, Pierrick, Miceli, Tia • Journal (2025)

Surrogate Models (49.9%) Novel Applications (49.5%) Tools & Libraries (100.0%)

Electron neutrino selection with deep neural networks for the ICARUS experiment

Koh, Dae Heun • Thesis (2025)

Novel Applications (39.0%) Tools & Libraries (32.7%)

Explainable physics-based constraints on reinforcement learning for accelerator controls

Colen, Jonathan, Schram, Malachi, Rajput, Kishansingh, Kasparian, Armen • arXiv (2025)

Reinforcement Learning & Autonomous Systems (60.7%) Novel Applications (49.1%) Tools & Libraries (100.0%)

First Experimental Demonstration of Machine Learning-Based Tuning on the PSI Injector 2 Cyclotron

Haj Tahar, M., Joho, W., Solodko, E., Bocchio, M., Marquie, S., Busch, M., Barchetti, A., Grillenberger, J., Snuverink, J., Schneider, M. • arXiv (2025)

Novel Applications (55.9%) Reinforcement Learning & Autonomous Systems (53.1%) Tools & Libraries (100.0%)

Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power

Korten, Till, Rybnikov, Vladimir, Vogt, Mathias, Roensch-Schulenburg, Juliane, Steinbach, Peter, Mirian, Najmeh • Journal (2024)

Novel Applications (33.4%) Tools & Libraries (100.0%)

High-Resolution Image Reconstruction with Unsupervised Learning and Noisy Data Applied to Ion-Beam Dynamics for Particle Accelerators

Francis Osswald, Mohammed Chahbaoui, X. Liang • Journal of Instrumentation (2026)

Novel Applications (42.7%) Statistics & Trends (35.5%) Reviews (100.0%) Tools & Libraries (100.0%)

Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment

Sytov, A., Cassou, K., Kubytskyi, V., Lenivenko, M., Huber, A. • arXiv (2025)

Novel Applications (48.7%) Beamline Design & Simulation (46.7%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Machine Learning approach to classifying quench antenna signals

Plebani, Alberto, Barzi, Emanuela, Teyber, Reed • Thesis (2025)

Novel Applications (36.0%) By Facility Type (33.0%)

Machine Learning-Based Graph Simplification for Symbolic Accelerators

Tiffany Yu, Rye Stahle-Smith, Darssan Eswaramoorthi, Rasha Karakchi • ArXiv.org (2026)

Tools & Libraries (44.3%) Novel Applications (43.3%)

Machine learning-based particle accelerator modeling

Emmanuel Goutierre • Laboratoire Interdisciplinaire des Sciences du Numérique (2024)

Novel Applications (52.4%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Machine learning-based prediction of magnet errors in storage ring light sources

Xu, Jianhao • arXiv (2025)

Novel Applications (32.0%) Beam Dynamics (27.1%) Tools & Libraries (100.0%)

Machine learning-based virtual diagnostics of dielectric laser acceleration

Thilo Egenolf, Oliver Boine-Frankenheim • arXiv (Cornell University) (2026)

Novel Applications (36.5%) Tools & Libraries (33.1%)

Machine Learning for superconducting magnets application

Stabilini, Elisa • Thesis (2025)

Novel Applications (39.6%) RF Systems (34.0%) Tools & Libraries (100.0%)

Machine Learning Methods for Tau Lepton Identification and Search for the Supersymmetric Partner of the Tau Lepton Using CMS Run 2 Data

Shchedrolosiev, Mykyta • Thesis (2025)

Novel Applications (33.8%) Tools & Libraries (100.0%)

Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach

Ibrahim, Anwar, Derkach, Denis, Petrenko, Alexey, Ratnikov, Fedor, Kaledin, Maxim • arXiv (2025)

Reinforcement Learning & Autonomous Systems (65.8%) Novel Applications (57.6%) Tools & Libraries (100.0%)

Optimizing Beam-Plasma Interactions Through Jitter Analysis Using Start-to-End Simulations

Hwang, Robin • arXiv (2024)

Novel Applications (54.1%) Beamline Design & Simulation (50.5%) Tools & Libraries (100.0%)

Particle identification in the GlueX detector with machine learning

Habjan, Eric, Dube, Richard, McIntyre, James, Edo, Mezmur, Jones, Richard • arXiv (2025)

Tools & Libraries (44.0%) Novel Applications (43.7%)

Physics-aware modelling of an accelerated particle cloud

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag • MLPS 2023 - Machine Learning and the Physical Sciences Workshop 23023 - At the 37th conference on Neural Information Processing Systems (NeurIPS) (2023)

Novel Applications (49.8%) Tools & Libraries (47.8%)

PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training

Shenghao Yang, Zhichao Wang, Oleg Balabanov, N. Benjamin Erichson, Michael W. Mahoney • arXiv (Cornell University) (2026)

Tools & Libraries (38.4%) Novel Applications (34.6%)

Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach

Ibrahim, Anwar, Petrenko, Alexey, Kaledin, Maxim, Suleiman, Ehab, Ratnikov, Fedor, Derkach, Denis • arXiv (2025)

Reinforcement Learning & Autonomous Systems (60.6%) Novel Applications (56.0%) Tools & Libraries (100.0%)

Road map for the tuning of hadronic interaction models with accelerator-based and astroparticle data

Albrecht, J., Becker Tjus, J., Behling, N., Blazek, J., Bleicher, M., Boelhauve, J., Cazon, L., Conceicao, R., Dembinski, H., Dietrich, L., Ebr, J., Ellbracht, J., Engel, R., Fedynitch, A., Fieg, M., Garzelli, M.V., Gaudu, C., Graziani, G., Gutjahr, P., Haungs, A., Huege, T., Hymon, K., Hünnefeld, Mirco, Kampert, K.-H., Kardum, L., Kolk, L., Korneeva, N., Kröninger, K., Maire, A., Menjo, H., Morejon, L., Ostapchenko, S., Paakkinen, P., Pierog, T., Plotko, P., Prosekin, A., Pyras, L., Pöschl, T., Rautenberg, Julian, Reininghaus, M., Rhode, W., Riehn, F., Roth, M., Sandrock, A., Sarcevic, I., Schmelling, M., Sigl, G., Sjöstrand, T., Soldin, D., Unger, M., Utheim, M., Vícha, J., Werner, K., Windau, M.E., Zhukov, V. • arXiv (2025)

Novel Applications (43.8%) Beamline Design & Simulation (40.8%) Tools & Libraries (100.0%)

Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation

Kane, G., Drobniak, P., Kazamias, S., Kubytskyi, V., Lenivenko, M., Lucas, B., Serhal, J., Cassou, K., Beck, A., Specka, A., Massimo, F. • arXiv (2024)

Beamline Design & Simulation (43.0%) Novel Applications (42.4%) Surrogate Models (100.0%)

Third-integer Resonant Extraction Regulation System for Mu2e

Narayanan, Aakaash • InspireHEP (2024)

Novel Applications (43.8%) Tools & Libraries (36.4%) Reviews (100.0%)

Towards Agentic AI on Particle Accelerators

Sulc, Antonin, Hellert, Thorsten, Kammering, Raimund, Houscher, Hayden, St. John, Jason • Journal (2024)

Reinforcement Learning & Autonomous Systems (59.8%) Novel Applications (58.9%) Tools & Libraries (100.0%)

Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators

Hemkant Nehete, Gaurav Verma, Amit Monga, Alok Kumar Shukla, Shailendra Yadav, Brajesh Kumar Kaushik • Unknown Venue (2026) • DOI: 10.1002/9781394336203.ch5

Novel Applications (57.0%) Tools & Libraries (55.3%)

Leveraging Subgraph Extraction for Performance Portable Programming Frameworks on DL Accelerators

Xiao Zhang, Huiying Lan, Tian Zhi • 15th IFIP International Conference on Network and Parallel Computing (NPC) (2018) • DOI: 10.1007/978-3-030-05677-3_21

Tools & Libraries (100.0%) Novel Applications (45.4%)

NNia-8: An 8-Core RISC-V Neural Network Inference Accelerator with Efficient Processing Elements and Memory Utilization

Xingbo Wang, Yucong Huang, Xinyu Kang, Yuru Li, Qi Wang, Terry Tao Ye • Lecture Notes in Computer Science (2025) • DOI: 10.1007/978-3-032-10466-3_23

Tools & Libraries (42.7%) Novel Applications (38.9%)

Artificial Intelligence and Machine Learning Based Predictive Modelling of Beam and 3D Bolt Through FEA

Neha M. Deshmukh, Yashwant S. Munde, Surajit S. Wadagaonkar, Avinash S. Shinde, Prashant R. Anerao • Lecture Notes in Mechanical Engineering (2025) • DOI: 10.1007/978-981-96-7576-0_1

Novel Applications (38.7%) Statistics & Trends (35.4%)

A Machine Learning Approach to Li-Ion Battery Module

Ram U. Kalyankar, Kalpak R. Sagar, Vipul M. Patel, Jyotirmay Banerjee, Hemantkumar B. Mehta • Lecture Notes in Mechanical Engineering (2025) • DOI: 10.1007/978-981-96-8508-0_21

Novel Applications (30.0%) Tools & Libraries (29.4%)

Publisher Correction: Fine-Tuning Universal Machine-Learned Interatomic Potentials for Applications in the Science of Steels

Naveen K. Mohandas, Sebastián Echeverri Restrepo, Marcel H. F. Sluiter • Journal of Phase Equilibria and Diffusion (2026) • DOI: 10.1007/s11669-026-01241-7

Novel Applications (32.0%) Statistics & Trends (31.7%)

Innovative Approaches for Learning Electrical Concepts in Physics

Papun Mondal, Sanjoy Kumar Pal, Pradipta Panchadhyayee, Suman Kumar Dash, Subhash Chandra Samanta • Resonance (2025) • DOI: 10.1007/s12045-025-1864-4

Novel Applications (40.8%) Statistics & Trends (26.0%)

Automatic detection of loose bolts in pipeline structures based on anti-noise mel cepstrum differential features and deep learning

Yixuan Chen, Hongyi Chen, Jingyi Wei, Jianchao Wu • Journal of Civil Structural Health Monitoring (2026) • DOI: 10.1007/s13349-025-01048-z

Data Management (33.6%) Novel Applications (31.3%)

Machine learning based parametrization of the resolution function for the first experimental area of the n_TOF facility at CERN

Petar Žugec, Marta Sabaté-Gilarte, Michael Bacak, Vasilis Vlachoudis, Adria Casanovas, Francisco García-Infantes • Nuclear Science and Techniques (2025) • DOI: 10.1007/s41365-025-01820-2

Novel Applications (35.7%) Tools & Libraries (100.0%)

Machine learning as a service system for particle accelerator and its application in CSNS

Mei, Hao, Zhang, Yuliang, Peng, Na, Cheng, Sinong, He, Yongcheng, Xue, Kangjia, Wang, Lin, Li, Mingtao, Wu, Xuan, Zhu, Peng • Radiation Detection Technology and Methods (2025) • DOI: 10.1007/s41605-025-00527-7

Tools & Libraries (100.0%) Novel Applications (60.5%)

A machine learning based approach to online electron reconstruction at CLAS12

Tyson, R., Gavalian, G. • Comput.Phys.Commun. (2026) • DOI: 10.1016/j.cpc.2026.110032

Novel Applications (39.3%) Data Management (38.5%) Tools & Libraries (100.0%)

A Survey of Accelerator Architectures for Deep Neural Networks

Yiran Chen, Yuan Xie, Linghao Song, Fan Chen, Tianqi Tang • Engineering (2020) • DOI: 10.1016/j.eng.2020.01.007

Novel Applications (60.5%) Tools & Libraries (55.0%) Reviews (100.0%)

Temporal-frequency enhanced fourier neural operator and collaborative adversarial learning for multi-physics-coupled transient simulation of natural gas pipelines

Yuqian Wang, Gang Liu, Xiaojing Zhu, Jiaxing Zou, Lei Chen • Expert Systems with Applications (2026) • DOI: 10.1016/j.eswa.2025.130550

Novel Applications (34.5%) Reinforcement Learning & Autonomous Systems (27.9%)

Hybrid machine learning-monte carlo approach for neutron shielding in high-energy proton facilities

Sariyer, Demet • J.Subatomic Part.Cosmol. (2026) • DOI: 10.1016/j.jspc.2026.100309

Novel Applications (38.5%) Statistics & Trends (36.2%) Tools & Libraries (100.0%)

Method for reconstruction the energy spectrum of a linear electron accelerator using neural networks

A.A. Kim, F.R. Studenikin, V.V. Khankin, S.A. Zolotov, A.P. Lyubomudrov, A.P. Chernyaev • Radiation Physics and Chemistry (2026) • DOI: 10.1016/j.radphyschem.2026.113775

Novel Applications (52.7%) Beamline Design & Simulation (51.7%)

Geoff: The Generic Optimization Framework & Frontend for Particle Accelerator Controls

Madysa, Penelope, Appel, Sabrina, Kain, Verena, Schenk, Michael • SoftwareX (2025) • DOI: 10.1016/j.softx.2025.102335

Tools & Libraries (100.0%) Novel Applications (57.6%)

Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning

Kaiser, Jan, Xu, Chenran, Eichler, Annika, Santamaria Garcia, Andrea, Stein, Oliver, Bründermann, Erik, Kuropka, Willi, Dinter, Hannes, Mayet, Frank, Vinatier, Thomas, Burkart, Florian, Schlarb, Holger • Sci.Rep. (2024) • DOI: 10.1038/s41598-024-66263-y

Reinforcement Learning & Autonomous Systems (58.3%) Novel Applications (48.5%)

Integrating physics and machine learning for unified seismic forward modeling and reservoir property inversion

Yeheya Zayier, Yaxiaer Yalikun, Yuanfeng Cheng, Dinghui Yang • Scientific Reports (2026) • DOI: 10.1038/s41598-026-36501-6

Novel Applications (35.5%) Reinforcement Learning & Autonomous Systems (30.8%)

Real-Time event reconstruction for Nuclear Physics Experiments using Artificial Intelligence

Gavalian, Gagik • EPJ Web Conf. (2025) • DOI: 10.1051/epjconf/202533701010

Novel Applications (43.6%) Tools & Libraries (100.0%)

Online Electron Reconstruction at CLAS12

Tyson, Richard, Gavalian, Gagik • EPJ Web Conf. (2025) • DOI: 10.1051/epjconf/202533701016

Novel Applications (36.5%) Tools & Libraries (100.0%)

Machine Learning for Optimized Polarization at Jefferson Lab

Jeske, Torri, Kasparian, Armen, Lawrence, David, Britton, Thomas, Schram, Malachi, Moran, Patrick, Fanelli, Cristiano, Guo, Jiawei, Jarvis, Naomi, Maxwell, James, Keith, Chris • EPJ Web Conf. (2025) • DOI: 10.1051/epjconf/202533701223

Novel Applications (35.1%) Statistics & Trends (29.7%) Surrogate Models (100.0%)

Reconstruction of Neutron Spectra Using Silicon Carbide Detectors in Monoenergetic Fields with Machine Learning Approach

Enrica Belfiore, Achment Chalil, Mehdi Ben Mosbah, Rodolphe Antoni, Olivier Llido, Abdallah Lyoussi, Christophe Destouches, Tomáš Slavíček, Andrea Šagátová, Jean-Emmanuel Groetz • Springer Link (Chiba Institute of Technology) (2025) • DOI: 10.1051/epjconf/202533810017/pdf

Novel Applications (35.7%) Statistics & Trends (33.6%)

Artificial intelligence for advancing particle accelerators

Ghribi, Adnan, Cassou, Kevin, Dalena, Barbara, Eichler, Annika, Guler, Hayg, Mistry, Andrew K., Oeftiger, Adrian, Shea, Thomas, Valentino, Gianluca, Welsch, Carsten P. • Europhys.News (2025) • DOI: 10.1051/epn/2025106

Novel Applications (68.4%) Tools & Libraries (100.0%) Reviews (100.0%)

Parameter estimation of three-phase voltage source converter using physics-informed machine learning

Cuiyu Liu, Zhiming Yang, Yipeng Yang, Ziwen Xiao, Chuanfeng Li, Yang Yu • Measurement Science and Technology (2025) • DOI: 10.1088/1361-6501/ae15ae

Novel Applications (31.2%) Tools & Libraries (100.0%)

Data-driven beam diagnostics using betatron radiation

Saberi, Hossein, Burvill, Jasper, Siddle, Carys, Xia, Guoxing, Patrick Farmer, John, Pukhov, Alexander • Plasma Phys.Control.Fusion (2026) • DOI: 10.1088/1361-6587/ae3eb3

Novel Applications (50.0%) Statistics & Trends (44.7%)

Leveraging reinforcement learning, genetic algorithms and transformers for background determination in particle physics

Novel Applications (42.4%) Reinforcement Learning & Autonomous Systems (42.2%)

Noise reduction for self-emission x-ray images in ICF using a physics-informed deep learning framework

Rui Hua, Tianzhu Gong, Chuankui Sun, Chengmin Lan, Yanxu Su, Liang Guo, Changyin Sun • Nuclear Fusion (2026) • DOI: 10.1088/1741-4326/ae4b51

Novel Applications (32.8%) Tools & Libraries (100.0%)

Machine learning for reducing noise in RF control signals at industrial accelerators

Henderson, M., Edelen, J.P., Einstein-Curtis, J., Hall, C.C., Cruz, J.A. Diaz, Edelen, A.L. • JINST (2024) • DOI: 10.1088/1748-0221/19/11/p11015

Novel Applications (53.6%) Tools & Libraries (46.7%) Reviews (100.0%)

Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators

Rajput, Kishansingh, Schram, Malachi, Edelen, Auralee, Colen, Jonathan, Kasparian, Armen, Roussel, Ryan, Carpenter, Adam, Zhang, He, Benesch, Jay • Mach.Learn.Sci.Tech. (2024) • DOI: 10.1088/2632-2153/adc221

Reinforcement Learning & Autonomous Systems (48.7%) Novel Applications (44.8%) Surrogate Models (100.0%)

Outlook towards deployable continual learning for particle accelerators

Rajput, Kishansingh, Lin, Sen, Edelen, Auralee, Blokland, Willem, Schram, Malachi • Mach.Learn.Sci.Tech. (2025) • DOI: 10.1088/2632-2153/adeb45

Novel Applications (53.0%) Data Management (52.5%) Reviews (100.0%)

Explainable physics-based constraints on reinforcement learning for accelerator optimization

Jonathan Colen, Malachi Schram, Kishansingh Rajput, Armen Kasparian • Machine Learning: Science and Technology (2026) • DOI: 10.1088/2632-2153/ae2fa8

Reinforcement Learning & Autonomous Systems (59.8%) Novel Applications (49.6%) Tools & Libraries (100.0%)

Machine learning time propagators for time-dependent density functional theory simulations

Karan Shah, Attila Cangi • Machine Learning: Science and Technology (2026) • DOI: 10.1088/2632-2153/ae68d4

Novel Applications (37.3%) Reinforcement Learning & Autonomous Systems (33.3%)

Machine Learning for Particle Accelerators

Elena Fol, Auralee Edelen • Unknown Venue (2025) • DOI: 10.1093/acrefore/9780190871994.013.138

Novel Applications (63.8%) Statistics & Trends (59.0%)

Symplectic machine learning model for fast simulation of space-charge effects

Wan, Jinyu, Qiang, Ji, Hao, Yue • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/bhpv-bcqk

Novel Applications (44.0%) Tools & Libraries (100.0%)

Reconstructing time-of-flight detector values of angular streaking using machine learning

Meier, David, Viefhaus, Jens, Hartmann, Gregor, Helml, Wolfram, Otto, Thorsten, Sick, Bernhard • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/csvm-858f

Beam Diagnostics (39.2%) Novel Applications (30.4%)

Beam-based identification of magnetic field errors in a synchrotron using deep Lie map networks

Caliari, Conrad, Oeftiger, Adrian, Boine-Frankenheim, Oliver • Phys.Rev.Accel.Beams (2024) • DOI: 10.1103/physrevaccelbeams.28.024601

Novel Applications (44.6%) Reinforcement Learning & Autonomous Systems (36.4%) Tools & Libraries (100.0%)

Virtual pulse reconstruction diagnostic for single-shot measurement of free electron laser radiation power

Korten, Till, Rybnikov, Vladimir, Steinbach, Peter, Mirian, Najmeh • Phys.Rev.Accel.Beams (2024) • DOI: 10.1103/physrevaccelbeams.28.030703

Novel Applications (40.0%) Tools & Libraries (100.0%)

Data-driven gradient optimization for field emission management in a superconducting radio-frequency linac

Goldenberg, Steven, Ahammed, Kawser, Carpenter, Adam, Li, Jiang, Suleiman, Riad, Tennant, Chris • Phys.Rev.Accel.Beams (2024) • DOI: 10.1103/physrevaccelbeams.28.044603

RF Systems (41.3%) Novel Applications (40.0%)

Survey and Benchmarking of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner • Unknown Venue (2019) • DOI: 10.1109/hpec.2019.8916327

Tools & Libraries (61.2%) Novel Applications (60.3%) Reviews (100.0%)

Reinforcement Learning for Charged Particle Beam Control to Minimize Injection Mismatch in Particle Accelerators

Balasooriya, Thilina, Yoo, Shinjae, Schoefer, Vincent, Tseng, Huan-Hsin, Gao, Yuan, Lin, Weijian, Silva, Chanaka De • Journal (2025) • DOI: 10.1109/icassp49660.2025.10889501

Reinforcement Learning & Autonomous Systems (56.9%) Novel Applications (46.9%)

Ultraefficient Compressed Phonocardiogram Classification on a Custom Embedded Neural Accelerator

Domenico Ragusa, Rens Baeyens, Danilo Pau, Elisa Marenzi, Jan Steckel, Walter Daems, Francesco Leporati, Emanuele Torti • IEEE Internet of Things Journal (2026) • DOI: 10.1109/jiot.2025.3635785

Novel Applications (39.4%) Surrogate Models (35.5%)

Adaptive Control Meets Deep Learning: EXTREMUM SEEKING IN LATENT SPACE WITH PARTICLE ACCELERATOR APPLICATIONS

Alexander Scheinker • IEEE Control Systems (2026) • DOI: 10.1109/mcs.2025.3646850

Reinforcement Learning & Autonomous Systems (46.1%) Novel Applications (43.9%)

Development of an Experimental Test Stand and Machine Learning Surrogate Models for Capturing Complex Responses in Pulsed Septa

Kawa, Krzysztof, Szumlak, Tomasz, Yagci, Omer Yusuf, Balhan, Bruno, Lackner, Friedrich • IEEE Trans.Appl.Supercond. (2026) • DOI: 10.1109/tasc.2026.3660099

Novel Applications (36.1%) Anomaly Detection & Fault Prediction (36.1%) Surrogate Models (100.0%)

Towards efficient machine-learning-based reduction of the cosmic-ray induced background in X-ray imaging detectors: increasing context awareness

Poliszczuk, Artem, Wilkins, Dan, Allen, Steven W., Miller, Eric D., Chattopadhyay, Tanmoy, Schneider, Benjamin, Darve, Julien Eric, Bautz, Marshall, Falcone, Abe, Foster, Richard, Grant, Catherine E., Herrmann, Sven, Kraft, Ralph, Morris, R. Glenn, Nulsen, Paul, Orel, Peter, Schellenberger, Gerrit, Stueber, Haley R. • Proc.SPIE Int.Soc.Opt.Eng. (2024) • DOI: 10.1117/12.3020598

Novel Applications (31.0%) Tools & Libraries (29.4%)

Laser wakefield electron acceleration simulation using physics-informed diffusion probabilistic models

Jech, Matej, Kovalenko, Alexander, Lazzarini, Carlo Maria, Grittani, Gabriele Maria • Proc.SPIE Int.Soc.Opt.Eng. (2025) • DOI: 10.1117/12.3058379

Novel Applications (50.2%) Statistics & Trends (44.4%)

Hybrid analytical-machine learning model for data-efficient prediction of ultrafast laser ablation

Eric P. Mottay • Laser-based Micro- and Nanoprocessing XX (2026) • DOI: 10.1117/12.3079308

Novel Applications (30.4%) Statistics & Trends (29.4%)

Research on the application of machine learning algorithms in aeromagnetic survey

Li Liu, Lei Zhou, Qingfeng Xu • Seventh International Conference on Geoscience and Remote Sensing Mapping (GRSM 2025) (2026) • DOI: 10.1117/12.3096466

Novel Applications (35.7%) Statistics & Trends (30.3%) Reviews (100.0%)

Machine Learning in High-Momentum Particle Identification in the MPD Experiment

Papoyan, V., Aparin, A., Ayriyan, A., Grigorian, H., Korobitsin, A. • Phys.Part.Nucl. (2025) • DOI: 10.1134/s1063779625700881

Tools & Libraries (44.5%) Novel Applications (43.1%)

Understanding error propagation in deep learning neural network (DNN) accelerators and applications

Guanpeng Li, Siva Kumar Sastry Hari, Michael B. Sullivan, Timothy Tsai, Karthik Pattabiraman, Joel Emer, Stephen W. Keckler • Unknown Venue (2017) • DOI: 10.1145/3126908.3126964

Novel Applications (41.7%) Reinforcement Learning & Autonomous Systems (36.7%)

A Teachable Machine for Transformers

Nicolas Pope, Matti Tedre • Proceedings of the 25th Koli Calling International Conference on Computing Education Research (2025) • DOI: 10.1145/3769994.3770061

Novel Applications (30.4%) Operations & Control (28.0%)

(<i>Invited</i>) Analog Signal Processing Technologies for Power-Efficient Neural Network Inference and Training

Bert Jan Offrein • Meeting abstracts/Meeting abstracts (Electrochemical Society. CD-ROM) (2025) • DOI: 10.1149/ma2025-01311590mtgabs

Novel Applications (41.0%) Tools & Libraries (38.5%)

Machine learning application for particle accelerator optimization-a review

Isti Dian Rachmawati, Nazrul Effendy, Taufik Taufik • IAES International Journal of Artificial Intelligence (2025) • DOI: 10.11591/ijai.v14.i4.pp3014-3021

Novel Applications (62.3%) Tools & Libraries (55.1%) Reviews (100.0%)

Machine learning for quantum control and information processing in trapped-ion systems

LI JiaChong, DING GeYi, YUAN Quan, YUAN MingZhe, FENG Mang • SCIENTIA SINICA Physica, Mechanica & Astronomica (2026) • DOI: 10.1360/sspma-2025-0278

Novel Applications (36.0%) Reinforcement Learning & Autonomous Systems (32.1%)

Graph Neural Networks for particle tracking in NA62 Experiment

Plini, L., Tinti, G., Spadaro, T., Galasso, F. • Nuovo Cim.C (2025) • DOI: 10.1393/ncc/i2025-25149-3

Novel Applications (38.5%) Tools & Libraries (34.8%)

Applying the machine learning methods to determine the linear optics parameters in the ThomX collector ring

Klekots, D., Bezshyyko, O., Golinka-Bezshyyko, L., Kubytskyi, V., Chaikovska, I. • Nucl.Phys.Atom.Energy (2024) • DOI: 10.15407/jnpae2024.04.394

Novel Applications (45.8%) Statistics & Trends (43.6%)

Study on estimation of internal cracks using electromagnetic waves and machine learning

Hinata Yamamoto, Hitoshi Ito, Toshiaki Mizobuchi • Proceedings of the Institution of Civil Engineers - Construction Materials (2025) • DOI: 10.1680/jcoma.24.00099

Anomaly Detection & Fault Prediction (31.9%) Novel Applications (31.0%)

Current applications of artificial intelligence and machine learning in oncologic ICU management

Shivam Dubey • Onco Critical Care (2026) • DOI: 10.18231/j.occ.70716.1770187942

Novel Applications (37.4%) Data Management (34.8%)

Automated beam tuning of the TRIUMF ISAC facility

Shelbaya, Olivier, Jung, Paul, Kester, Oliver, Hassan, Omar • JACoW (2025) • DOI: 10.18429/jacow-hiat2025-tux02

Novel Applications (48.7%) Beamline Design & Simulation (44.4%) Tools & Libraries (100.0%)

Towards continual machine learning for particle accelerators

Rajput, Kishansingh, Schram, Malachi, Blokland, Willem, Zhukov, Alexander, Lin, Sen • JACoW (2026) • DOI: 10.18429/jacow-ibic2025-tuai02

Novel Applications (53.4%) Tools & Libraries (50.1%)

Beam diagnostics, data acquisition system, and applications of machine learning at the KEK e-/e+ Linac

Miyahara, Fusashi, Kinoshita, Kaito, Kurata, Masakazu, Iwasaki, Masako, Natsui, Takuya, Okayasu, Yuichi, Satake, Itsuka, Satoh, Masanori, Uemura, Kosuke, Wang, Di • JACoW (2026) • DOI: 10.18429/jacow-ibic2025-tubc02

Novel Applications (45.5%) Statistics & Trends (43.7%)

Modelization of an Injector With Machine Learning

Mathieu Debongnie, Maud Baylac, Frédéric Bouly, Nicolas Chauvin, Angélique Gatera, Tomas Junquera, Didier Uriot • 10th International Particle Accelerator Conference (2019) • DOI: 10.18429/jacow-ipac2019-wepts006

Novel Applications (50.6%) Beamline Design & Simulation (50.2%)

A Machine Learning Technique for Dynamic Aperture Computation

Barbara Dalena, Mehdi Ben Ghali • 12th International Particle Accelerator Conference (2021) • DOI: 10.18429/jacow-ipac2021-thpab201

Novel Applications (45.2%) Statistics & Trends (40.0%)

Surrogate Model for Linear Accelerator: A fast Neural Network approximation of ThomX's simulator

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag • IPAC 2023 - 14th International Particle Accelerator Conference (2023) • DOI: 10.18429/jacow-ipac2023-thpl039

Novel Applications (57.0%) Beamline Design & Simulation (56.9%) Reviews (100.0%) Surrogate Models (100.0%)

Updates to Xopt for online accelerator optimization and control

Roussel, Ryan, Kennedy, Dylan, Boltz, Tobias, Baker, Kathryn, Mayes, Christopher, Edelen, Auralee • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-thpg85

Tools & Libraries (100.0%) Novel Applications (55.3%) Surrogate Models (100.0%)

High fidelity numerical modelling and condition monitoring applied to septum magnets at CERN

Kawa, Krzysztof, Szumlak, Tomasz, Kokkinos, Charilaos, Lackner, Friedrich • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-thpr29

Beamline Design & Simulation (41.8%) Novel Applications (41.2%) Reviews (100.0%)

Machine learning-based extraction of longitudinal beam parameters in the LHC

Iliakis, Konstantinos, Karlsen-Bæck, Birk Emil, Trad, Georges, Timko, Helga, Zampetakis, Michail, Argyropoulos, Theodoros • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-tups56

Novel Applications (44.7%) Beamline Design & Simulation (43.6%)

The reinforcement learning for autonomous accelerators collaboration

Santamaria Garcia, Andrea, Eichler, Annika, Xu, Chenran, Kaiser, Jan, Scomparin, Luca, Schenk, Michael, Pochaba, Sabrina, Hirlaender, Simon • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-tups62

Reinforcement Learning & Autonomous Systems (67.0%) Novel Applications (55.8%)

Breaking new ground in data-intensive science: first insights from the LIV.INNO center for doctoral training

Welsch, Carsten • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-tups74

Novel Applications (57.9%) Statistics & Trends (54.3%)

Machine learning approach to MDI optimization for 3 TeV c.o.m. Muon Collider

Castelli, Luca • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-mopm092

Novel Applications (40.0%) Statistics & Trends (35.1%)

Beam Energy Forecasting using Machine Learning at the CLEAR accelerator

Gilardi, Antonio, Malyzhenkov, Alexander, Petersson,Alfred, Mostacci Andrea, Pollastro, Andrea, Aksoy, Avni, Filippetto, Daniele, Gamba,Davide, Granados, Eduardo, Tangari,Giacomo, Sjobak, Kyrre, Bonnard,Ladislas, Wroe,Laurence, Carranza-García, Manuel, Franek Ondrej, Korysko,Pierre, Corsini Roberto, Rieker,Vilde, Farabolini, Wilfrid, Mazzoni, Stefano • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thpm031

Novel Applications (44.9%) Statistics & Trends (39.8%)

Advancing accelerator science through data-intensive research and training

Welsch, Carsten • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thps110

Novel Applications (68.2%) Data Management (61.8%)

Advances in machine learning inference of dynamic aperture evaluation for the LHC

Montanari,Carlo Emilio, Di Croce, Davide, Van der Veken,Frederik, Giovannozzi, Massimo, Appleby, Robert, Redaelli, Stefano, Pieloni, Tatiana • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-wepm062

Novel Applications (42.6%) Statistics & Trends (38.6%)

Advancements in backwards differentiable beam dynamics simulations for accelerator design, model calibration, and machine learning

Roussel, Ryan, Edelen, Auralee, Gonzalez-Aguilera, Juan Pablo, Lehe, Remi, Huebl, Axel, Kaiser, Jan, Santamaria Garcia, Andrea, Xu, Chenran, Eichler, Annika, Charleux, Grégoire • JACoW (2024) • DOI: 10.18429/jacow-linac2024-thpb068

Beamline Design & Simulation (54.2%) Novel Applications (53.2%) Tools & Libraries (100.0%)

Beam emittance and Twiss parameters from pepper-pot images using physically informed neural nets

Knight, Ian, Mustapha, Brahim • JACoW (2024) • DOI: 10.18429/jacow-linac2024-tuaa005

Novel Applications (38.2%) Beam Dynamics (34.6%)

Machine learning-based non-destructive measurement of bunch length at FRIB

Wan, Jinyu, Plastun, Alexander, Ostroumov, Peter • JACoW (2024) • DOI: 10.18429/jacow-linac2024-tupb006

Beam Diagnostics (47.2%) Novel Applications (37.4%)

Advanced algorithms for linear accelerator design and operation

Ong, Ysabella Kassandra, Bellan, Luca, Pisent, Andrea, Comunian, Michele, Fagotti, Enrico, Bortolato, Damiano, Montis, Maurizio, Giacchini, Mauro, Carletto, Osvaldo • JACoW (2024) • DOI: 10.18429/jacow-linac2024-tupb075

Novel Applications (58.4%) Statistics & Trends (55.5%) Surrogate Models (100.0%)

Adaptive machine learning with hard physics constraints and generative diffusion for 6D phase space diagnostics

Scheinker, Alexander • JACoW (2024) • DOI: 10.18429/jacow-linac2024-wexa005

Novel Applications (52.6%) Tools & Libraries (100.0%)

Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems

Rautela, Mahindra, Scheinker, Alexander • arXiv (2026) • DOI: 10.18429/jacow-napac2025-mop002

Novel Applications (43.0%) Tools & Libraries (100.0%)

A self-supervised transformer for RF cavity signal denoising

Rajesh, Vikshar, Edelen, Auralee, Hall, Christopher, O'Shea, Finn, Edelen, Jonathan • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop006

Novel Applications (35.8%) Reinforcement Learning & Autonomous Systems (30.9%)

Automated RF Phase Adjustment for Beam Stabilization in the Fermilab Linac

Chichili, R.R., Sulskis, J.A., Sharankova, R., Vamanan, B., Ravi, S. • Journal (2025) • DOI: 10.18429/jacow-napac2025-mop011

Novel Applications (37.6%) Statistics & Trends (31.8%)

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

Novel Applications (37.3%) Optimization & Control (36.1%)

Machine learning assisted Bayesian calibration of accelerator digital twin from orbit response data

Lin, Weijian, Kelly, Christopher, Hoffstaetter, Georg, Brown, Kevin, Urban, Nathan • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop056

Surrogate Models (55.7%) Novel Applications (44.7%)

Machine learning at the Spallation Neutron Source accelerator and target

Armen Kasparian, Willem Blokland, Alexander Zhukov, Anant Ray, Drew Winder, Kishansingh Rajput, Malachi Schram • JACOW (2026) • DOI: 10.18429/jacow-napac2025-mop057

Novel Applications (47.9%) Statistics & Trends (44.1%)

Machine learning-enhanced infrared imaging for temperature anomaly detection in power supplies

Mohsen, Osama, Lobach, Ihar, Borland, Michael, Sun, Yine • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop059

Anomaly Detection & Fault Prediction (45.9%) Novel Applications (37.0%)

Integrating simulation and machine learning for Proton Storage Ring beam analysis

Leon, Christopher, Huang, En-Chuan, Scheinker, Alexander, Anisimov, Petr M. • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop065

Beam Diagnostics (47.7%) Novel Applications (43.7%)

Machine learning-driven computations of 3D Coherent Synchrotron Radiation

Leon, Christopher, Scheinker, Alexander, Yampolsky, Nikolai, Anisimov, Petr M. • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop097

Novel Applications (41.5%) By Facility Type (37.1%)

Online optimizations of NSLS-II Linac and Linac-to-Booster beam lines using machine learning methods

Song, Minghao, Wang, Guimei, Hidaka, Yoshiteru, Yang, Xi • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop103

Novel Applications (44.7%) Beamline Design & Simulation (42.6%)

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

Pathak, Abhishek, Hanlet, Pierrick, Miceli, Tia • JACoW (2026) • DOI: 10.18429/jacow-napac2025-thp028

Surrogate Models (53.1%) Novel Applications (51.3%) Tools & Libraries (100.0%)

JuTrack, a Julia-based auto-differentiable accelerator simulation code for advanced dynamics, scientific machine learning and optimization

Wan, Jinyu, Alamprese, Helena, Ratcliff, Christian, Qiang, Ji, Hao, Yue • JACoW (2026) • DOI: 10.18429/jacow-napac2025-tup056

Beamline Design & Simulation (55.3%) Novel Applications (53.8%) Tools & Libraries (100.0%)

Towards differentiable beam dynamics modeling in BLAST/ImpactX

Huebl, Axel, Mitchell, Chad, Lehe, Remi, Charleux, Grégoire, Myers, Andrew, Zhang, Weiqun, Qiang, Ji, Vay, Jean-Luc, Kaiser, Jan, Hespe, Christian, Gonzalez-Aguilera, Juan Pablo, Xu, Chenran, Santamaria Garcia, Andrea, Roussel, Ryan, Edelen, Auralee, Moses, William Steven • JACoW (2026) • DOI: 10.18429/jacow-napac2025-tup101

Novel Applications (46.0%) Tools & Libraries (46.0%)

Machine learning-enhanced deterministic controls in lasers and accelerators

Dan Wang, Alessio Amodio, Alex Picksley, A. J. Gonsalves, Curtis J. Berger, Hai-En Tsai, Jeroen van Tilborg, Mahek Logantha, N. R. Vora, Qiang Du, Russell Wilcox, Samuel K. Barber, Tong Zhou, Z. Eisentraut • JACOW (2026) • DOI: 10.18429/jacow-napac2025-weyn01

Reinforcement Learning & Autonomous Systems (52.0%) Novel Applications (48.0%)

Safe extremum seeking for real-time adaptive accelerator control

Williams, Alan, Scheinker, Alexander • JACoW (2026) • DOI: 10.18429/jacow-napac2025-weyn02

Novel Applications (55.4%) Reinforcement Learning & Autonomous Systems (49.0%)

Continual Learning for Particle Accelerators

Nuclear Physics (NP) USDOE Office of Science (SC), M. Schram, Thomas Jefferson National Accelerator Facility (TJNAF), Sen Lin, Kishansingh Rajput, Alexander Zhukov, Willem Blokland • Unknown Venue (2026) • DOI: 10.2172/3012392

Reinforcement Learning & Autonomous Systems (56.4%) Novel Applications (55.3%)

Software Upgrades for the High-Luminosity LHC

Shope, David Richard • PoS (2025) • DOI: 10.22323/1.478.0193

Tools & Libraries (54.9%) Novel Applications (54.5%)

Particle Tracking in the NA62 Experiment

Plini, Leonardo, Tinti, Gemma, Spadaro, Tommaso, Spinelli, Indro, Galasso, Fabio • PoS (2025) • DOI: 10.22323/1.489.0043

Novel Applications (38.7%) Data Management (36.9%)

A Hybrid Monte Carlo–Machine Learning Framework for High-Energy Neutron Shielding Using Boron-Enhanced Concrete

Demet Sariyer, Elif Yıldırım • International Journal of Computational and Experimental Science and Engineering (2026) • DOI: 10.22399/ijcesen.4827

Novel Applications (30.2%) Statistics & Trends (26.8%) Tools & Libraries (100.0%)

Machine learning

Snuverink, Jochem • CERN Yellow Rep.School Proc. (2024) • DOI: 10.23730/cyrsp-2024-003.2131

Novel Applications (70.3%) Tools & Libraries (100.0%) Reviews (100.0%)

Deep Lie Map Networks:A novel Approach to Infer Nonlinear Synchrotron Optics from Beam Oscillations

Caliari, Conrad • Thesis (2025) • DOI: 10.26083/tuprints-00029370

Novel Applications (38.3%) Beam Dynamics (36.6%) Tools & Libraries (100.0%)

Rapid Secondary Neutron Dose Estimation in Proton Accelerators Using FLUKA and Machine Learning

Demet Sariyer, Elif Yıldırım • Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi (2026) • DOI: 10.29233/sdufeffd.1783848

Statistics & Trends (44.7%) Novel Applications (44.6%)

Assessing the Performance of Deep Learning Predictions for Dynamic Aperture of a Hadron Circular Particle Accelerator

Di Croce, Davide, Giovannozzi, Massimo, Montanari, Carlo Emilio, Pieloni, Tatiana, Redaelli, Stefano, Van der Veken, Frederik F. • Instruments (2024) • DOI: 10.3390/instruments8040050

Novel Applications (49.4%) Tools & Libraries (100.0%) Surrogate Models (100.0%)

Unsupervised Online Learning for AC Optimal Power Flow: A Gradient-Guided Physics-Informed Neural Network Approach

Bozhen Jiang, Jing Qu, Qin Wang • Institute of Electrical and Electronics Engineers (IEEE) (2025) • DOI: 10.36227/techrxiv.176127198.84709317/v1

Novel Applications (33.5%) Reinforcement Learning & Autonomous Systems (32.0%)

Architectural Taxonomy of AI Accelerators: A Memory and Interconnect Centric Perspective

Nour Abu Jassar • Unknown Venue (2026) • DOI: 10.36227/techrxiv.177102159.95058346/v1

Novel Applications (64.9%) Tools & Libraries (60.6%)

Technology Topic Mining and Patent Data Analysis of NdFeB Permanent Magnet Materials Based on Machine Learning

Jian-zhong LIU, Yang BAI, Xin-fang ZHANG, Xiao-yu LIU, Zhen-jun JIN, Ke WANG, Meng-cheng ZHOU • Chinese Rare Earths (2026) • DOI: 10.3724/s1004-0277.20250007

Statistics & Trends (33.7%) Novel Applications (30.8%)

First Experimental Demonstration of Machine Learning-Based Tuning on the PSI Injector 2 Cyclotron

Sandrine Marquié • arXiv (Cornell University) (2025) • DOI: 10.48550/arxiv.2512.03829

Novel Applications (40.8%) Optimization & Control (39.6%)

EventQueues: Autodifferentiable spike event queues for brain simulation on AI accelerators

Amirreza Movahedin, Said Hamdioui • arXiv (Cornell University) (2025) • DOI: 10.48550/arxiv.2512.05906

Novel Applications (50.8%) Tools & Libraries (48.0%)

PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training

Shenghao Yang, Zhichao Wang, Oleg Balabanov, N. Benjamin Erichson, Michael W. Mahoney • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2601.22137

Tools & Libraries (38.4%) Novel Applications (34.6%)

Machine learning-based virtual diagnostics of dielectric laser acceleration

Thilo Egenolf, Oliver Boine-Frankenheim • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2603.28549

Novel Applications (36.5%) Tools & Libraries (33.1%)

Machine Learning-Based Graph Simplification for Symbolic Accelerators

Tiffany Yu, Rye Stahle-Smith, Darssan Eswaramoorthi, Rasha Karakchi • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2605.08996

Tools & Libraries (44.3%) Novel Applications (43.3%)

A complete discussion on fully reconfigurable, digital, scalable, graph and sparsity-aware near-memory accelerator for graph neural networks

Siddhartha Raman Sundara Raman, LIZY JOHN, Jaydeep P. Kulkarni • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2605.19405

Novel Applications (36.8%) Tools & Libraries (35.4%)

Machine Learning Applications for Improving Accelerator Operations

Lin, Lucy • Thesis (2024) • DOI: 10.7298/2e46-7a49

Novel Applications (62.6%) Statistics & Trends (57.3%)

Machine learning applied to single-shot x-ray diagnostics in an XFEL

A. Sanchez-Gonzalez, P. Micaelli, C. Olivier, T. R. Barillot, M. Ilchen, A. A. Lutman, A. Marinelli, T. Maxwell, A. Achner, M. Agåker, N. Berrah, C. Bostedt, J. Buck, P. H. Bucksbaum, S. Carron Montero, B. Cooper, J. P. Cryan, M. Dong, R. Feifel, L. J. Frasinski, H. Fukuzawa, A. Galler, G. Hartmann, N. Hartmann, W. Helml, A. S. Johnson, A. Knie, A. O. Lindahl, J. Liu, K. Motomura, M. Mucke, C. O'Grady, J-E. Rubensson, E. R. Simpson, R. J. Squibb, C. Såthe, K. Ueda, M. Vacher, D. J. Walke, V. Zhaunerchyk, R. N. Coffee, J. P. Marangos • arXiv (2016)

Novel Applications (30.3%) By Facility Type (29.7%) Tools & Libraries (100.0%)

Neural Networks for Modeling and Control of Particle Accelerators

A. L. Edelen, S. G. Biedron, B. E. Chase, D. Edstrom, S. V. Milton, P. Stabile • arXiv (2016)

Novel Applications (53.9%) Operations & Control (53.8%)

Accelerating the BSM interpretation of LHC data with machine learning

Gianfranco Bertone, Marc Peter Deisenroth, Jong Soo Kim, Sebastian Liem, Roberto Ruiz de Austri, Max Welling • arXiv (2016)

Novel Applications (41.2%) Data Management (39.1%) Tools & Libraries (100.0%)

Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets

Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik • arXiv (2017)

Novel Applications (37.7%) Statistics & Trends (33.1%)

First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

A. L. Edelen, S. G. Biedron, S. V. Milton, J. P. Edelen • arXiv (2016)

Beam Diagnostics (42.3%) Novel Applications (42.2%)

Machine learning for analysis of plasma driven Ion source

N. Joshi, O. Meusel, H. Podlech • arXiv (2018)

Novel Applications (53.1%) Statistics & Trends (46.1%) Tools & Libraries (100.0%)

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń, Ernesto De Matteis • arXiv (2017)

Novel Applications (41.1%) Anomaly Detection & Fault Prediction (38.2%)

Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems

Auralee Edelen, Nicole Neveu, Yannick Huber, Mattias Frey, Christopher Mayes, Andreas Adelmann • arXiv (2020)

Novel Applications (51.5%) Tools & Libraries (100.0%) Surrogate Models (100.0%)

Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks

Artem Maevskiy, Denis Derkach, Nikita Kazeev, Andrey Ustyuzhanin, Maksim Artemev, Lucio Anderlini • arXiv (2019)

Data Management (41.1%) Novel Applications (40.8%)

Generative Adversarial Networks (GAN) for compact beam source modelling in Monte Carlo simulations

David Sarrut, Nils Krah, Jean-Michel Létang • arXiv (2019)

Novel Applications (35.9%) Statistics & Trends (31.5%)

Machine learning for design optimization of storage ring nonlinear dynamics

Faya Wang, Minghao Song, Auralee Edelen, Xiaobiao Huang • arXiv (2019)

Optimization & Control (43.7%) Novel Applications (31.2%) Surrogate Models (100.0%)

Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin • arXiv (2020)

RF Systems (49.8%) Novel Applications (36.4%)

Introduction to Machine Learning for Accelerator Physics

Daniel Ratner • arXiv (2020)

Novel Applications (61.5%) Tools & Libraries (100.0%)

Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators

Andrei Ivanov, Ilya Agapov • arXiv (2020)

Novel Applications (49.3%) Beamline Design & Simulation (43.2%)

Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics

A. Hanuka, C. Emma, T. Maxwell, A. Fisher, B. Jacobson, M. J. Hogan, Z. Huang • arXiv (2020)

Beam Diagnostics (39.9%) Novel Applications (37.8%) Tools & Libraries (100.0%)

Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning

Xiaoying Pang, Sunil Thulasidasan, Larry Rybarcyk • arXiv (2020)

Reinforcement Learning & Autonomous Systems (65.2%) Novel Applications (52.3%) Tools & Libraries (100.0%)

Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster

Jason St. John, Christian Herwig, Diana Kafkes, Jovan Mitrevski, William A. Pellico, Gabriel N. Perdue, Andres Quintero-Parra, Brian A. Schupbach, Kiyomi Seiya, Nhan Tran, Malachi Schram, Javier M. Duarte, Yunzhi Huang, Rachael Keller • arXiv (2021)

Reinforcement Learning & Autonomous Systems (58.1%) Novel Applications (55.7%) Surrogate Models (100.0%)

Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL

Simon Hirlaender, Niky Bruchon • arXiv (2022)

Reinforcement Learning & Autonomous Systems (58.2%) Novel Applications (51.5%)

BOOSTR: A Dataset for Accelerator Control Systems

Diana Kafkes, Jason St. John • arXiv (2021)

Novel Applications (53.2%) Data Management (51.3%)

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Sichen Li, Mélissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani, Andreas Adelmann • arXiv (2021)

Novel Applications (46.5%) Statistics & Trends (43.6%)

Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators

Alexander Scheinker, Frederick Cropp, Sergio Paiagua, Daniele Filippetto • arXiv (2021)

Novel Applications (42.8%) Data Management (36.2%) Tools & Libraries (100.0%)

Improving Surrogate Model Accuracy for the LCLS-II Injector Frontend Using Convolutional Neural Networks and Transfer Learning

Lipi Gupta, Auralee Edelen, Nicole Neveu, Aashwin Mishra, Christopher Mayes, Young-Kee Kim • arXiv (2021)

Novel Applications (50.9%) Beamline Design & Simulation (46.4%) Surrogate Models (100.0%)

SPIRAL2 Cryomodules Models: a Gateway to Process Control and Machine Learning

Adrien Vassal, Adnan Ghribi, François Millet, François Bonne, Patrick Bonnay, Pierre-Emmanuel Bernaudin • arXiv (2021)

Beamline Design & Simulation (42.1%) Novel Applications (41.7%)

Graph Generative Models for Fast Detector Simulations in High Energy Physics

Ali Hariri, Darya Dyachkova, Sergei Gleyzer • arXiv (2021)

Novel Applications (42.2%) Tools & Libraries (42.1%)

Adaptive Latent Space Tuning for Non-Stationary Distributions

Alexander Scheinker, Frederick Cropp, Sergio Paiagua, Daniele Filippetto • arXiv (2021)

Novel Applications (39.8%) Tools & Libraries (100.0%)

Uncertainty Quantification for Virtual Diagnostic of Particle Accelerators

Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka • arXiv (2021)

Novel Applications (42.6%) Statistics & Trends (40.0%) Tools & Libraries (100.0%)

Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks

Friedrich Bethke, Richard Pausch, Patrick Stiller, Alexander Debus, Michael Bussmann, Nico Hoffmann • arXiv (2021)

Surrogate Models (100.0%) Novel Applications (42.4%)

Fast, efficient and flexible particle accelerator optimisation using densely connected and invertible neural networks

Renato Bellotti, Romana Boiger, Andreas Adelmann • arXiv (2021)

Novel Applications (59.7%) Tools & Libraries (100.0%) Surrogate Models (100.0%)

Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning

Alexander Scheinker • arXiv (2021)

Novel Applications (47.9%) Tools & Libraries (100.0%)

Quantifying Uncertainty for Machine Learning Based Diagnostic

Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka • arXiv (2021)

Novel Applications (37.7%) Statistics & Trends (35.2%) Tools & Libraries (100.0%)

Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction

Cenk Tüysüz, Carla Rieger, Kristiane Novotny, Bilge Demirköz, Daniel Dobos, Karolos Potamianos, Sofia Vallecorsa, Jean-Roch Vlimant, Richard Forster • arXiv (2021)

Novel Applications (32.6%) By Facility Type (30.0%)

Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator

Willem Blokland, Pradeep Ramuhalli, Charles Peters, Yigit Yucesan, Alexander Zhukov, Malachi Schram, Kishansingh Rajput, Torri Jeske • arXiv (2021)

Novel Applications (46.8%) Anomaly Detection & Fault Prediction (45.1%)

Anomaly Detection in Particle Accelerators using Autoencoders

Jonathan P. Edelen, Nathan M. Cook • arXiv (2021)

Anomaly Detection & Fault Prediction (41.9%) Novel Applications (41.6%) Tools & Libraries (100.0%)

Physics-informed neural network method for modelling beam-wall interactions

Kazuhiro Fujita • arXiv (2022)

Beamline Design & Simulation (52.5%) Novel Applications (49.2%)

A Neural Network Model of a Quasi-Periodic Elliptically Polarizing Undulator in Universal Mode

Ryan Sheppard, Cameron Baribeau, Tor Pedersen, Mark Boland, Drew Bertwistle • arXiv (2022)

Novel Applications (50.5%) Reinforcement Learning & Autonomous Systems (38.6%)

Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron Laser

J. Zhu, N. M. Lockmann, M. K. Czwalinna, H. Schlarb • arXiv (2022)

Novel Applications (37.5%) Data Management (36.1%)

Differentiable Preisach Modeling for Characterization and Optimization of Accelerator Systems with Hysteresis

R. Roussel, A. Edelen, D. Ratner, K. Dubey, J. P. Gonzalez-Aguilera, Y. K. Kim, N. Kuklev • arXiv (2022)

Novel Applications (52.2%) Beamline Design & Simulation (51.3%)

Adaptive Machine Learning for Time-Varying Systems: Towards 6D Phase Space Diagnostics of Short Intense Charged Particle Beams

Alexander Scheinker, Spencer Gessner • arXiv (2022)

Novel Applications (51.4%) Statistics & Trends (43.0%) Tools & Libraries (100.0%)

Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-based Charged Particle Beams

Auralee Edelen, Christopher Mayes • arXiv (2022)

Novel Applications (52.4%) Beamline Design & Simulation (47.3%)

Optimizing a Superconducting Radiofrequency Gun Using Deep Reinforcement Learning

David Meier, Luis Vera Ramirez, Jens Völker, Bernhard Sick, Jens Viefhaus, Gregor Hartmann • arXiv (2022)

Reinforcement Learning & Autonomous Systems (44.6%) Novel Applications (41.3%)

Towards Reliable Neural Generative Modeling of Detectors

Lucio Anderlini, Matteo Barbetti, Denis Derkach, Nikita Kazeev, Artem Maevskiy, Sergei Mokhnenko • arXiv (2022)

Novel Applications (39.6%) Tools & Libraries (36.5%)

AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider

C. Fanelli, Z. Papandreou, K. Suresh, J. K. Adkins, Y. Akiba, A. Albataineh, M. Amaryan, I. C. Arsene, C. Ayerbe Gayoso, J. Bae, X. Bai, M. D. Baker, M. Bashkanov, R. Bellwied, F. Benmokhtar, V. Berdnikov, J. C. Bernauer, F. Bock, W. Boeglin, M. Borysova, E. Brash, P. Brindza, W. J. Briscoe, M. Brooks, S. Bueltmann, M. H. S. Bukhari, A. Bylinkin, R. Capobianco, W. -C. Chang, Y. Cheon, K. Chen, K. -F. Chen, K. -Y. Cheng, M. Chiu, T. Chujo, Z. Citron, E. Cline, E. Cohen, T. Cormier, Y. Corrales Morales, C. Cotton, J. Crafts, C. Crawford, S. Creekmore, C. Cuevas, J. Cunningham, G. David, C. T. Dean, M. Demarteau, S. Diehl, N. Doshita, R. Dupre, J. M. Durham, R. Dzhygadlo, R. Ehlers, L. El Fassi, A. Emmert, R. Ent, R. Fatemi, S. Fegan, M. Finger, M. Finger Jr., J. Frantz, M. Friedman, I. Friscic, D. Gangadharan, S. Gardner, K. Gates, F. Geurts, R. Gilman, D. Glazier, E. Glimos, Y. Goto, N. Grau, S. V. Greene, A. Q. Guo, L. Guo, S. K. Ha, J. Haggerty, T. Hayward, X. He, O. Hen, D. W. Higinbotham, M. Hoballah, T. Horn, A. Hoghmrtsyan, P. -h. J. Hsu, J. Huang, G. Huber, A. Hutson, K. Y. Hwang, C. Hyde, M. Inaba, T. Iwata, H. S. Jo, K. Joo, N. Kalantarians, G. Kalicy, K. Kawade, S. J. D. Kay, A. Kim, B. Kim, C. Kim, M. Kim, Y. Kim, Y. Kim, E. Kistenev, V. Klimenko, S. H. Ko, I. Korover, W. Korsch, G. Krintiras, S. Kuhn, C. -M. Kuo, T. Kutz, J. Lajoie, D. Lawrence, S. Lebedev, H. Lee, J. S. H. Lee, S. W. Lee, Y. -J. Lee, W. Li, W. B. Li, X. Li, X. Li, X. Li, X. Li, Y. T. Liang, S. Lim, C. -h. Lin, D. X. Lin, K. Liu, M. X. Liu, K. Livingston, N. Liyanage, W. J. Llope, C. Loizides, E. Long, R. -S. Lu, Z. Lu, W. Lynch, D. Marchand, M. Marcisovsky, P. Markowitz, H. Marukyan, P. McGaughey, M. Mihovilovic, R. G. Milner, A. Milov, Y. Miyachi, A. Mkrtchyan, P. Monaghan, R. Montgomery, D. Morrison, A. Movsisyan, H. Mkrtchyan, A. Mkrtchyan, C. Munoz Camacho, M. Murray, K. Nagai, J. Nagle, I. Nakagawa, C. Nattrass, D. Nguyen, S. Niccolai, R. Nouicer, G. Nukazuka, M. Nycz, V. A. Okorokov, S. Oresic, J. D. Osborn, C. O'Shaughnessy, S. Paganis, S. F. Pate, M. Patel, C. Paus, G. Penman, M. G. Perdekamp, D. V. Perepelitsa, H. Periera da Costa, K. Peters, W. Phelps, E. Piasetzky, C. Pinkenburg, I. Prochazka, T. Protzman, M. L. Purschke, J. Putschke, J. R. Pybus, R. Rajput-Ghoshal, J. Rasson, B. Raue, K. F. Read, K. Roed, R. Reed, J. Reinhold, E. L. Renner, J. Richards, C. Riedl, T. Rinn, J. Roche, G. M. Roland, G. Ron, M. Rosati, C. Royon, J. Ryu, S. Salur, N. Santiesteban, R. Santos, M. Sarsour, J. Schambach, A. Schmidt, N. Schmidt, C. Schwarz, J. Schwiening, R. Seidl, A. Sickles, P. Simmerling, S. Sirca, D. Sharma, Z. Shi, T. -A. Shibata, C. -W. Shih, S. Shimizu, U. Shrestha, K. Slifer, K. Smith, D. Sokhan, R. Soltz, W. Sondheim, J. Song, J. Song, I. I. Strakovsky, P. Steinberg, P. Stepanov, J. Stevens, J. Strube, P. Sun, X. Sun, V. Tadevosyan, W. -C. Tang, S. Tapia Araya, S. Tarafdar, L. Teodorescu, A. Timmins, L. Tomasek, N. Trotta, R. Trotta, T. S. Tveter, E. Umaka, A. Usman, H. W. van Hecke, C. Van Hulse, J. Velkovska, E. Voutier, P. K. Wang, Q. Wang, Y. Wang, Y. Wang, D. P. Watts, N. Wickramaarachchi, L. Weinstein, M. Williams, C. -P. Wong, L. Wood, M. H. Wood, C. Woody, B. Wyslouch, Z. Xiao, Y. Yamazaki, Y. Yang, Z. Ye, H. D. Yoo, M. Yurov, N. Zachariou, W. A. Zajc, W. Zha, J. Zhang, Y. Zhang, Y. X. Zhao, X. Zheng, P. Zhuang • arXiv (2022)

Novel Applications (54.7%) Tools & Libraries (100.0%) Reviews (100.0%)

Predicting Beam Transmission Using 2-Dimensional Phase Space Projections Of Hadron Accelerators

Anthony Tran, Yue Hao, Brahim Mustapha, Jose L. Martinex Marin • arXiv (2022)

Beamline Design & Simulation (40.5%) Novel Applications (37.7%)

Transverse phase space tomography in the CLARA accelerator test facility using image compression and machine learning

Andrzej Wolski, Mark A. Johnson, Matthew King, Boris L. Militsyn, Peter H. Williams • arXiv (2022)

Novel Applications (41.8%) Statistics & Trends (39.1%)

Diagnostics for Linac Optimization With Machine Learning

R. Sharankova, M. Mwaniki, K. Seiya, M. Wesley • arXiv (2022)

Novel Applications (48.9%) Statistics & Trends (46.7%)

Automatic setup of 18 MeV electron beamline using machine learning

Francesco Maria Velotti, Brennan Goddard, Verena Kain, Rebecca Ramjiawan, Giovanni Zevi Della Porta, Simon Hirlaender • arXiv (2022)

Novel Applications (44.3%) Reinforcement Learning & Autonomous Systems (43.1%)

Uncertainty Aware ML-based surrogate models for particle accelerators: A Study at the Fermilab Booster Accelerator Complex

Malachi Schram, Kishansingh Rajput, Karthik Somayaji Peng Li, Jason St. John, Himanshu Sharma • arXiv (2022)

Novel Applications (43.2%) Statistics & Trends (43.1%) Surrogate Models (100.0%)

Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators

Sichen Li, Andreas Adelmann • arXiv (2022)

Statistics & Trends (51.5%) Novel Applications (49.7%) Reviews (100.0%)

Machine learning-based analysis of experimental electron beams and gamma energy distributions

M. Yadav, M. Oruganti, S. Zhang, B. Naranjo, G. Andonian, Y. Zhuang, Ö. Apsimon, C. P. Welsch, J. B. Rosenzweig • arXiv (2023)

Novel Applications (44.8%) Statistics & Trends (40.1%)

Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning

Majdi I. Radaideh, Chris Pappas, Mark Wezensky, Pradeep Ramuhalli, Sarah Cousineau • arXiv (2022)

Anomaly Detection & Fault Prediction (41.9%) Novel Applications (31.9%)

Applications of object detection networks at high-power laser systems and experiments

Jinpu Lin, Florian Haberstroh, Stefan Karsch, Andreas Döpp • arXiv (2022)

Novel Applications (38.0%) Tools & Libraries (100.0%)

Neural Network Prior Mean for Particle Accelerator Injector Tuning

Connie Xu, Ryan Roussel, Auralee Edelen • arXiv (2022)

Novel Applications (47.5%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Applications of Differentiable Physics Simulations in Particle Accelerator Modeling

Ryan Roussel, Auralee Edelen • arXiv (2022)

Beamline Design & Simulation (57.4%) Novel Applications (54.4%)

Data-driven Science and Machine Learning Methods in Laser-Plasma Physics

Andreas Döpp, Christoph Eberle, Sunny Howard, Faran Irshad, Jinpu Lin, Matthew Streeter • arXiv (2023)

Novel Applications (42.5%) Statistics & Trends (37.5%)

Identification of Magnetic Field Errors in Synchrotrons based on Deep Lie Map Networks

Conrad Caliari, Adrian Oeftiger, Oliver Boine-Frankenheim • arXiv (2023)

Novel Applications (52.9%) Statistics & Trends (43.8%)

Physics-constrained 3D Convolutional Neural Networks for Electrodynamics

Alexander Scheinker, Reeju Pokharel • arXiv (2023)

Novel Applications (33.3%) Tools & Libraries (26.1%)

Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments

Novel Applications (36.4%) Tools & Libraries (100.0%)

Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions

Steven Goldenberg, Malachi Schram, Kishansingh Rajput, Thomas Britton, Chris Pappas, Dan Lu, Jared Walden, Majdi I. Radaideh, Sarah Cousineau, Sudarshan Harave • arXiv (2023)

Novel Applications (34.8%) Statistics & Trends (32.8%)

Time-drift Aware RF Optimization with Machine Learning Techniques

R. Sharankova, M. Mwaniki, K. Seiya, M. Wesley • arXiv (2023)

Novel Applications (51.8%) Statistics & Trends (47.1%)

Beam Detection Based on Machine Learning Algorithms

Haoyuan Li, Qing Yin • arXiv (2023)

Beam Diagnostics (42.1%) Novel Applications (34.6%)

Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source

Ryan Humble, William Colocho, Finn O'Shea, Daniel Ratner, Eric Darve • arXiv (2023)

Anomaly Detection & Fault Prediction (39.3%) Novel Applications (31.8%)

Variational Autoencoders for Noise Reduction in Industrial LLRF Systems

J. P. Edelen, M. J. Henderson, J. Einstein-Curtis, C. C. Hall, J. A. Diaz Cruz, A. L. Edelen • arXiv (2023)

Novel Applications (54.5%) Statistics & Trends (49.2%) Reviews (100.0%)

Plasma Surrogate Modelling using Fourier Neural Operators

Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt Kusner, Marc Peter Deisenroth, Anima Anandkumar, JOREK Team, MAST Team • arXiv (2024)

Novel Applications (34.9%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Machine Learning For Beamline Steering

Isaac Kante • arXiv (2023)

Novel Applications (51.3%) Tools & Libraries (45.9%)

Bayesian Optimization Algorithms for Accelerator Physics

Ryan Roussel, Auralee L. Edelen, Tobias Boltz, Dylan Kennedy, Zhe Zhang, Fuhao Ji, Xiaobiao Huang, Daniel Ratner, Andrea Santamaria Garcia, Chenran Xu, Jan Kaiser, Angel Ferran Pousa, Annika Eichler, Jannis O. Lubsen, Natalie M. Isenberg, Yuan Gao, Nikita Kuklev, Jose Martinez, Brahim Mustapha, Verena Kain, Weijian Lin, Simone Maria Liuzzo, Jason St. John, Matthew J. V. Streeter, Remi Lehe, Willie Neiswanger • arXiv (2024)

Statistics & Trends (50.2%) Novel Applications (49.8%) Reviews (100.0%) Surrogate Models (100.0%)

Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators

Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta • arXiv (2024)

Anomaly Detection & Fault Prediction (48.8%) Novel Applications (37.5%)

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e

Chenwei Xu, Jerry Yao-Chieh Hu, Aakaash Narayanan, Mattson Thieme, Vladimir Nagaslaev, Mark Austin, Jeremy Arnold, Jose Berlioz, Pierrick Hanlet, Aisha Ibrahim, Dennis Nicklaus, Jovan Mitrevski, Jason Michael St. John, Gauri Pradhan, Andrea Saewert, Kiyomi Seiya, Brian Schupbach, Randy Thurman-Keup, Nhan Tran, Rui Shi, Seda Ogrenci, Alexis Maya-Isabelle Shuping, Kyle Hazelwood, Han Liu • arXiv (2023)

Reinforcement Learning & Autonomous Systems (44.5%) Novel Applications (39.6%)

Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations

Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia • arXiv (2024)

Novel Applications (56.6%) Reinforcement Learning & Autonomous Systems (53.0%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Time-Delayed Koopman Network-Based Model Predictive Control for the FRIB RFQ

Jinyu Wan, Shen Zhao, Wei Chang, Yue Hao • arXiv (2024)

Novel Applications (35.7%) Reinforcement Learning & Autonomous Systems (32.1%) Tools & Libraries (100.0%)

Machine-learning approach for operating electron beam at KEK $e^-/e^+$ injector Linac

Gaku Mitsuka, Shinnosuke Kato, Naoko Iida, Takuya Natsui, Masanori Satoh • arXiv (2024)

Novel Applications (54.0%) Statistics & Trends (49.0%)

Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology

Zhiyuan Chen, Wei Lu, Radhika Bhong, Yimin Hu, Brian Freeman, Adam Carpenter • arXiv (2024)

Anomaly Detection & Fault Prediction (46.1%) Novel Applications (42.2%)

Combined track finding with GNN & CKF

Lukas Heinrich, Benjamin Huth, Andreas Salzburger, Tilo Wettig • arXiv (2024)

Data Management (33.6%) Novel Applications (30.3%)

Optimizing Dynamic Aperture Studies with Active Learning

D. Di Croce, M. Giovannozzi, E. Krymova, T. Pieloni, S. Redaelli, M. Seidel, R. Tomás, F. F. Van der Veken • arXiv (2024)

Novel Applications (53.9%) Statistics & Trends (47.8%) Tools & Libraries (100.0%)

Leveraging Prior Mean Models for Faster Bayesian Optimization of Particle Accelerators

Tobias Boltz, Jose L. Martinez, Connie Xu, Kathryn R. L. Baker, Zihan Zhu, Jenny Morgan, Ryan Roussel, Daniel Ratner, Brahim Mustapha, Auralee L. Edelen • arXiv (2025)

Novel Applications (50.1%) Statistics & Trends (46.0%)

GAIA: A General AI Assistant for Intelligent Accelerator Operations

Frank Mayet • arXiv (2024)

Tools & Libraries (100.0%) Novel Applications (50.0%)

Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language

Jan Kaiser, Annika Eichler, Anne Lauscher • arXiv (2024)

Tools & Libraries (57.8%) Novel Applications (54.3%)

Accelerator beam phase space tomography using machine learning to account for variations in beamline components

Andrzej Wolski, Diego Botelho, David Dunning, Amelia E. Pollard • arXiv (2024)

Novel Applications (47.4%) Statistics & Trends (42.4%)

Long Short-Term Memory Networks for Anomaly Detection in Magnet Power Supplies of Particle Accelerators

Ihar Lobach, Michael Borland • arXiv (2024)

Novel Applications (42.7%) Statistics & Trends (38.0%)

Accelerator system parameter estimation using variational autoencoded latent regression

Mahindra Rautela, Alan Williams, Alexander Scheinker • arXiv (2024)

Novel Applications (43.6%) Statistics & Trends (41.3%)

Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Abhishek Pathak • arXiv (2024)

Novel Applications (57.2%) Beamline Design & Simulation (50.2%) Tools & Libraries (100.0%)

Neural Networks for ID Gap Orbit Distortion Compensation in PETRA III

Bianca Veglia, Ilya Agapov, Joachim Keil • arXiv (2024)

Novel Applications (44.1%) Tools & Libraries (33.2%)

A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL

Lynda Boukela, Annika Eichler, Julien Branlard, Nur Zulaiha Jomhari • arXiv (2024)

Anomaly Detection & Fault Prediction (43.4%) Novel Applications (36.9%)

Beam-based Identification of Magnetic Field Errors in a Synchrotron using Deep Lie Map Networks

Conrad Caliari, Adrian Oeftiger, Oliver Boine-Frankenheim • arXiv (2024)

Novel Applications (43.8%) By Facility Type (37.7%) Tools & Libraries (100.0%)

Beamline Steering Using Deep Learning Models

Dexter Allen, Isaac Kante, Dorian Bohler • arXiv (2024)

Novel Applications (48.8%) Tools & Libraries (44.4%)

Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation

G. Kane, P. Drobniak, S. Kazamias, V. Kubytskyi, M. Lenivenko, B. Lucas, J. Serhal, K. Cassou, A. Beck, A. Specka, F. Massimo • arXiv (2025)

Novel Applications (47.8%) Beamline Design & Simulation (46.0%) Surrogate Models (100.0%)

Machine Learning for Reducing Noise in RF Control Signals at Industrial Accelerators

M. Henderson, J. P. Edelen, J. Einstein-Curtis, C. C. Hall, J. A. Diaz Cruz, A. L. Edelen • arXiv (2024)

Novel Applications (54.3%) Tools & Libraries (46.8%) Reviews (100.0%)

Towards Agentic AI on Particle Accelerators

Antonin Sulc, Thorsten Hellert, Raimund Kammering, Hayden Hoschouer, Jason St. John • arXiv (2025)

Reinforcement Learning & Autonomous Systems (59.8%) Novel Applications (58.9%) Tools & Libraries (100.0%)

Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators

Kamaljeet Singh, Kangkan Goswami, Raghunath Sahoo, Sumanta Samal • arXiv (2025)

Novel Applications (38.3%) Beamline Design & Simulation (37.1%)

Data-Driven Discovery of Beam Centroid Dynamics

Liam A. Pocher, Irving Haber, Thomas M. Antonsen Jr., Patrick G. O'Shea • arXiv (2024)

Novel Applications (48.3%) Beamline Design & Simulation (45.5%)

Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators

Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch • arXiv (2024)

Reinforcement Learning & Autonomous Systems (46.6%) Novel Applications (43.4%) Surrogate Models (100.0%)

Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac

Steven Goldenberg, Kawser Ahammed, Adam Carpenter, Jiang Li, Riad Suleiman, Chris Tennant • arXiv (2024)

RF Systems (40.7%) Novel Applications (39.5%)

Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power

Till Korten, Vladimir Rybnikov, Mathias Vogt, Juliane Roensch-Schulenburg, Peter Steinbach, Najmeh Mirian • arXiv (2024)

Novel Applications (33.4%) Tools & Libraries (100.0%)

A Realistic Collimated X-Ray Image Simulation Pipeline

Benjamin El-Zein, Dominik Eckert, Thomas Weber, Maximilian Rohleder, Ludwig Ritschl, Steffen Kappler, Andreas Maier • arXiv (2024)

Data Management (35.6%) Novel Applications (32.0%) Tools & Libraries (100.0%)

Variable Rate Neural Compression for Sparse Detector Data

Yi Huang, Yeonju Go, Jin Huang, Shuhang Li, Xihaier Luo, Thomas Marshall, Joseph Osborn, Christopher Pinkenburg, Yihui Ren, Evgeny Shulga, Shinjae Yoo, Byung-Jun Yoon • arXiv (2024)

Novel Applications (41.2%) Data Management (40.6%)

Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines

Siyu Wang, Shengran Dai, Jianhui Jiang, Shuang Wu, Yufei Peng, Junbin Zhang • arXiv (2024)

Reinforcement Learning & Autonomous Systems (43.1%) Novel Applications (32.3%)

Virtual Pulse Reconstruction Diagnostic for Single-Shot Measurement of Free Electron Laser Radiation Power

Till Korten, Vladimir Rybnikov, Peter Steinbach, Najmeh Mirian • arXiv (2024)

Novel Applications (40.9%) Tools & Libraries (100.0%)

Efficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling

Xihaier Luo, Samuel Lurvey, Yi Huang, Yihui Ren, Jin Huang, Byung-Jun Yoon • arXiv (2024)

Data Management (46.2%) Novel Applications (42.9%)

Optimizing Beam-Plasma Interactions Through Jitter Analysis Using Start-to-End Simulations

Robin Hwang • arXiv (2024)

Novel Applications (54.1%) Beamline Design & Simulation (50.5%) Tools & Libraries (100.0%)

Loss function to optimise signal significance in particle physics

Jai Bardhan, Cyrin Neeraj, Subhadip Mitra, Tanumoy Mandal • arXiv (2024)

Novel Applications (31.0%) Statistics & Trends (27.8%)

Inverse Surrogate Model of a Soft X-Ray Spectrometer using Domain Adaptation

Enrico Ahlers, Peter Feuer-Forson, Gregor Hartmann, Rolf Mitzner, Peter Baumgärtel, Jens Viefhaus • arXiv (2025)

Novel Applications (34.4%) Data Management (29.6%) Surrogate Models (100.0%)

Explainable physics-based constraints on reinforcement learning for accelerator controls

Jonathan Colen, Malachi Schram, Kishansingh Rajput, Armen Kasparian • arXiv (2025)

Reinforcement Learning & Autonomous Systems (60.7%) Novel Applications (49.1%) Tools & Libraries (100.0%)

Using Convolutional Neural Networks to Accelerate 3D Coherent Synchrotron Radiation Computations

Christopher Leon, Petr M. Anisimov, Nikolai Yampolsky, Alexander Scheinker • arXiv (2025)

Novel Applications (39.8%) By Facility Type (37.1%)

Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach

Anwar Ibrahim, Denis Derkach, Alexey Petrenko, Fedor Ratnikov, Maxim Kaledin • arXiv (2025)

Reinforcement Learning & Autonomous Systems (65.8%) Novel Applications (57.6%) Tools & Libraries (100.0%)

Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment

A. Sytov, K. Cassou, V. Kubytskyi, M. Lenivenko, A. Huber • arXiv (2025)

Novel Applications (48.7%) Beamline Design & Simulation (46.7%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Deep Learning Assisted Modeling for $χ^{(2)}$ Nonlinear Optics

Jack Hirschman, Erfan Abedi, Minyang Wang, Hao Zhang, Abhimanyu Borthakur, Justin Baker, Andrea L. Bertozzi, Randy Lemons, Sergio Carbajo • arXiv (2025)

Surrogate Models (100.0%) Novel Applications (35.0%) Tools & Libraries (100.0%)

AI-Assisted Transport of Radioactive Ion Beams

Sergio Lopez-Caceres, Daniel Santiago-Gonzalez • arXiv (2025)

Novel Applications (43.0%) Statistics & Trends (39.2%)

A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE

Aashwin Mishra, Matt Seaberg, Ryan Roussel, Fred Poitevin, Jana Thayer, Daniel Ratner, Auralee Edelen, Apurva Mehta • arXiv (2025)

Novel Applications (45.5%) Tools & Libraries (41.7%)

Geoff: The Generic Optimization Framework & Frontend for Particle Accelerator Controls

Penelope Madysa, Sabrina Appel, Verena Kain, Michael Schenk • arXiv (2025)

Tools & Libraries (100.0%) Novel Applications (57.6%)

Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence

Zixu Wang, Yuhan Wang, Junfei Ma, Fuyuan Wu, Junchi Yan, Xiaohui Yuan, Zhe Zhang, Jie Zhang • arXiv (2025)

Novel Applications (41.9%) Tools & Libraries (100.0%)

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry

Asif Iqbal, John Verboncoeur, Peng Zhang • arXiv (2025)

Novel Applications (38.9%) Statistics & Trends (38.7%) Tools & Libraries (100.0%)

A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network

Mianjun Xiao, Peng Song, Yulong Liu, Cedric Korte, Ziyang Xu, Jiale Gao, Jiaqi Lu, Haoyang Nie, Qiantong Deng, Timing Qu • arXiv (2025)

Novel Applications (30.0%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

FusionMAE: large-scale pretrained model to optimize and simplify diagnostic and control of fusion plasma

Zongyu Yang, Zhenghao Yang, Wenjing Tian, Jiyuan Li, Xiang Sun, Guohui Zheng, Songfen Liu, Niannian Wu, Rongpeng Li, Zhaohe Xu, Bo Li, Zhongbing Shi, Zhe Gao, Wei Chen, Xiaoquan Ji, Min Xu, Wulyu Zhong • arXiv (2025)

Novel Applications (35.6%) Data Management (31.2%)

Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach

Anwar Ibrahim, Alexey Petrenko, Maxim Kaledin, Ehab Suleiman, Fedor Ratnikov, Denis Derkach • arXiv (2025)

Reinforcement Learning & Autonomous Systems (60.6%) Novel Applications (56.0%) Tools & Libraries (100.0%)

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

A. Whitbeck, J. Berlioz, K. Danison-Fieldhouse, K. Hazelwood, M. Khan, J. Mitrevski, A. Narayanan, J. St. John, N. Tran, J. Ji, M. Walter • arXiv (2025)

Novel Applications (37.3%) Optimization & Control (36.1%)

Automated RF Phase Adjustment for Beam Stabilization in the Fermilab Linac

R. R. Chichili, J. A. Sulskis, R. Sharankova, B. Vamanan, S. Ravi • arXiv (2025)

Novel Applications (37.6%) Statistics & Trends (31.8%)

First Experimental Demonstration of Machine Learning-Based Tuning on the PSI Injector 2 Cyclotron

M. Haj Tahar, W. Joho, E. Solodko, M. Bocchio, S. Marquie, M. Busch, A. Barchetti, J. Grillenberger, J. Snuverink, M. Schneider • arXiv (2025)

Novel Applications (55.9%) Reinforcement Learning & Autonomous Systems (53.1%) Tools & Libraries (100.0%)

Machine learning-based prediction of magnet errors in storage ring light sources

Jianhao Xu • arXiv (2025)

Novel Applications (32.0%) Beam Dynamics (27.1%) Tools & Libraries (100.0%)

ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset

Doğa Elitez, Paul Gessinger, Daniel Murnane, Marcus Selchou Raaholt, Andreas Salzburger, Stine Kofoed Skov, Andreas Stefl, Anna Zaborowska • arXiv (2025)

Tools & Libraries (42.4%) Novel Applications (38.8%)

Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models

Guillermo Rodriguez-Llorente, Galo Gallardo, Rodrigo Morant Navascués, Nikita Khvatkin Petrovsky, Anderson Sabogal, Roberto Gómez-Espinosa Martín • arXiv (2025)

Novel Applications (46.0%) Reinforcement Learning & Autonomous Systems (45.9%) Surrogate Models (100.0%)

SE-MLP Model for Predicting Prior Acceleration Features in Penetration Signals

Yankang Li, Changsheng Li • arXiv (2025)

Novel Applications (30.6%) Surrogate Models (30.3%)

U-Net-based surrogate modeling for attosecond X-ray free-electron lasers

Yufei Wei, Bingyang Yan, Chenzhi Xu, Jiawei Yan, Haixiao Deng • arXiv (2026)

Surrogate Models (100.0%) Novel Applications (38.6%)

Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems

Mahindra Rautela, Alexander Scheinker • arXiv (2026)

Novel Applications (43.0%) Tools & Libraries (100.0%)

NOBLE: Accelerating Transformers with Nonlinear Low-Rank Branches

Ethan Smith • arXiv (2026)

Novel Applications (32.5%) Tools & Libraries (31.1%)

GNN For Muon Particle Momentum estimation

Vishak K Bhat, Eric A. F. Reinhardt, Sergei Gleyzer • arXiv (2026)

Tools & Libraries (39.2%) Novel Applications (35.8%)

High-Resolution Image Reconstruction with Unsupervised Learning and Noisy Data Applied to Ion-Beam Dynamics for Particle Accelerators

Francis Osswald, Mohammed Chahbaoui, Xinyi Liang • arXiv (2026)

Novel Applications (42.7%) Statistics & Trends (35.5%) Reviews (100.0%) Tools & Libraries (100.0%)

Characterization and upgrade of a quantum graph neural network for charged particle tracking

Matteo Argenton, Laura Cappelli, Concezio Bozzi • arXiv (2026)

Novel Applications (38.4%) By Facility Type (34.1%)

AI-Ready Control System for the Fermilab Accelerator Complex

Tia Miceli, Erik Gottschalk, Donovan Tooke, Evan Milton, Robert Santucci, Hayden Hoschouer, Michael Balcewicz, Jennifer Case, Abhishek Deshpande, Kit Fieldhouse, Sudeshna Ganguly, Beau Harrison, Aisha Ibrahim, Thomas Kobilarcik, Michael Olander, Abhishek Pathak, Jason St. John, Aaron Sauers • arXiv (2026)

Novel Applications (62.5%) Data Management (59.4%) Tools & Libraries (100.0%)

Machine learning-based virtual diagnostics of dielectric laser acceleration

Thilo Egenolf, Oliver Boine-Frankenheim • arXiv (2026)

Surrogate Models (100.0%) Novel Applications (43.3%) Tools & Libraries (100.0%)

Data-Driven Optimisation of Superconducting Magnets at CEA Paris-Saclay

Damien F. G. Minenna, Guillaume Dilasser, Robin Penavaire, Valerio Calvelli, Thibault de Chabannes, Thibault Lecrevisse, Thomas Achard, Jason Le Coz, Christophe Berriaud, Benoît Bolzon, Antomne Caunes, Phillipe Fazilleau, Hélène Felice, Clément Genot, Antoine Guinet, Nikola Jerance, François-Paul Juster, Thibaut Lemercier, Gilles Lenoir, Clément Lorin, Yann Perron, Camille Pucheu-Plante, Étienne Rochepault, Damien Simon, Francesco Stacchi, Michel Segreti, Vincent Trauchessec, Olivier Tuske, Hajar Zgour • arXiv (2026)

Novel Applications (55.6%) Data Management (47.6%)

Machine-State Embeddings as an Operational Coordinate System for Accelerator Operation

Chris Tennant, Jundong Li, Song Wang • arXiv (2026)

Novel Applications (40.2%) Data Management (39.3%) Reviews (100.0%)

Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC

Minghui Zhang, Xiaobin Li, Jie Chen, Ningtao Zhang, Fenhua Lu, Junrui Ma, Jiazhen Yan, Wanqin Tu, Xiaodong Tang, Bingshui Gao, Chengui Lu, Zhichao Zhang, Jinlong Zhang, Weiping Liu • arXiv (2026)

Novel Applications (30.3%) Tools & Libraries (27.2%)

RBF neural net based classifier for the AIRIX accelerator fault diagnosis

J. C. Ribes, G. Delaunay, J. Delvaux, E. Merle, M. Mouillet • arXiv (2000)

Anomaly Detection & Fault Prediction (42.2%) Novel Applications (34.5%) Tools & Libraries (100.0%)

Optimization

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Optimized Supervised Machine Learning for Accurate Estimation of Reinforcement in RC Beams and Columns

Nhan Thanh Vu Nguyen, Chon Tran, Duong Thai Le, Quy Thue Nguyen • Lecture Notes in Civil Engineering (2025) • DOI: 10.1007/978-3-032-04645-1_20

Reinforcement Learning & Autonomous Systems (41.1%) Optimization & Control (34.2%)

Optimization of the Claus Process Using Machine Learning and Real Operational Data: A Case Study in an SRU

Amirfarzam Shokouhalaei • Chemistry Africa (2026) • DOI: 10.1007/s42250-025-01560-1

Optimization & Control (31.3%) Reinforcement Learning & Autonomous Systems (30.8%)

Optimizing pulsed field ablation for cardiac arrhythmias integrating Taguchi method, machine learning and genetic algorithms

Ethan Nabuurs, Kuljeet Singh Grewal, Amara Sanchez, Mikkayla Ellsworth-Reid, Robert F. Gilmour, Roderick Melnik, Sundeep Singh • Results in Engineering (2026) • DOI: 10.1016/j.rineng.2026.108978

Optimization & Control (28.0%)

Application of ensemble machine learning algorithms and filtering techniques in slow orbit feedback systems of electron storage rings

Fan, Jiaqi, Liu, Weibin, Wang, Jiuqing, Wei, Yanru, Wei, Yuanyuan, Ji, Daheng • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/physrevaccelbeams.28.042801

Optimization & Control (36.3%) Reinforcement Learning & Autonomous Systems (35.4%) Tools & Libraries (100.0%)

TTBO-FL: Joint Training, Trajectory, and Beamforming Optimization for Energy-Efficient Federated Learning in UAV Swarm

Chunyu Zhang, Yanlu Li, Yiming Liu, Yuzhen Huang, Zhi Zhang • IEEE Internet of Things Journal (2026) • DOI: 10.1109/jiot.2025.3628900

Reinforcement Learning & Autonomous Systems (35.4%) Optimization & Control (28.8%)

Koopman-Operator-Based Control of Hypersonic Flight Vehicles With Few-Shot Learning for Internet of Aerospace Things

Wenjia Deng, Tingting Wang, Wencheng Zou, Jian Guo, Zhengrong Xiang • IEEE Internet of Things Journal (2026) • DOI: 10.1109/jiot.2026.3667989

Reinforcement Learning & Autonomous Systems (39.3%) Optimization & Control (32.4%)

Machine learning-driven inverse design of cosine beam honeycombs for zero Poisson's ratio and predetermined mechanical curves

Zi-Tao Wang, Hai-Tao Liu • International Journal of Applied Mechanics (2026) • DOI: 10.1142/s1758825126500535

Optimization & Control (32.9%)

Reinforcement Learning Concepts

Jonas Hellgren, Johannes Lindgren • Reinforcement Learning Explained (2026) • DOI: 10.1201/9781003634409-3

Reinforcement Learning & Autonomous Systems (46.5%) Optimization & Control (27.5%)

Machine, Deep, and Reinforcement Learning Algorithms and Applications

K. Gowri, B. L. Shivakumar • Real-Time Artificial Intelligence (AI) (2025) • DOI: 10.1201/9781998511358-3

Reinforcement Learning & Autonomous Systems (50.0%) Optimization & Control (36.3%)

Comparative evaluation of machine learning models for thrust force estimation in aluminum 5083 H111

Emre Teke, Neslihan Özsoy, Deniz Demircioğlu Diren, Murat Özsoy • Materials Testing (2026) • DOI: 10.1515/mt-2025-0389

Statistics & Trends (26.1%) Optimization & Control (25.4%) Tools & Libraries (100.0%)

Uncertainty-Quantified Machine Model Construction Using Physics-Informed Gaussian Processes and Bayesian Optimization

Isensee,Victoria, Oeftiger, Adrian, Boine-Frankenheim, Oliver • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thpm017

Statistics & Trends (28.9%) Optimization & Control (28.8%)

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

Novel Applications (37.3%) Optimization & Control (36.1%)

Hybrid Machine Learning based Optimization with Beamforming of 6G Mobile Radio Network for LEO Satellite base station in Urban Areas

Haymanot Derebe Bizuneh • Springer Science and Business Media LLC (2025) • DOI: 10.21203/rs.3.rs-8089868/v1

Optimization & Control (31.9%)

First Experimental Demonstration of Machine Learning-Based Tuning on the PSI Injector 2 Cyclotron

Sandrine Marquié • arXiv (Cornell University) (2025) • DOI: 10.48550/arxiv.2512.03829

Novel Applications (40.8%) Optimization & Control (39.6%)

Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities

Jorge Alberto Diaz Cruz, Sandra Biedron, Manel Martinez-Ramon, Salvador Sosa, Reza Pirayesh • arXiv (2019)

Optimization & Control (40.3%) RF Systems (36.9%) Tools & Libraries (100.0%)

Machine learning for design optimization of storage ring nonlinear dynamics

Faya Wang, Minghao Song, Auralee Edelen, Xiaobiao Huang • arXiv (2019)

Optimization & Control (43.7%) Novel Applications (31.2%) Surrogate Models (100.0%)

Online tuning and light source control using a physics-informed Gaussian process Adi

A. Hanuka, J. Duris, J. Shtalenkova, D. Kennedy, A. Edelen, D. Ratner, X. Huang • arXiv (2019)

Optimization & Control (46.2%) Operations & Control (42.3%)

TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning

Francesco Capuano, Davorin Peceli, Gabriele Tiboni, Raffaello Camoriano, Bedřich Rus • arXiv (2023)

Reinforcement Learning & Autonomous Systems (45.2%) Optimization & Control (35.9%) Tools & Libraries (100.0%)

Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Reinforcement Learning & Autonomous Systems (57.9%) Optimization & Control (46.4%)

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

A. Whitbeck, J. Berlioz, K. Danison-Fieldhouse, K. Hazelwood, M. Khan, J. Mitrevski, A. Narayanan, J. St. John, N. Tran, J. Ji, M. Walter • arXiv (2025)

Novel Applications (37.3%) Optimization & Control (36.1%)

Simulations

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Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models

Rodriguez-Llorente, Guillermo, Gallardo, Galo, Navascués, Rodrigo Morant, Petrovsky, Nikita Khvatkin, Sabogal, Anderson, Martín, Roberto Gómez-Espinosa • Journal (2025)

Novel Applications (46.0%) Reinforcement Learning & Autonomous Systems (45.9%) Surrogate Models (100.0%)

Data-Driven Discovery of Beam Centroid Dynamics

Pocher, Liam A., Haber, Irving, Antonsen, Thomas M., O'Shea, Patrick G. • arXiv (2024)

Novel Applications (48.3%) Beamline Design & Simulation (45.5%)

Digital Twin Framework for PIP-II Linac: AI-Driven Multi-Scale Modeling from Ion Source to 800 MeV

Pathak, Abhishek, Hanlet, Pierrick, Miceli, Tia • Journal (2025)

Surrogate Models (49.9%) Novel Applications (49.5%) Tools & Libraries (100.0%)

From Compact Plasma Particle Sources to Advanced Accelerators with Modeling at Exascale

Axel Huebl, Remi Lehe, Edoardo Zoni, Olga Shapoval, Ryan T Sandberg, Marco Garten, Arianna Formenti, Revathi Jambunathan, Prabhat Kumar, Kevin Gott, Andrew Myers, Weiqun Zhang, Ann Almgren, Chad E Mitchell, Ji Qiang, David Grote, Alexander Sinn, Severin Diederichs, Maxence Thevenet, Luca Fedeli, Thomas Clark, Neil Zaim, Henri Vincenti, Jean-Luc Vay • 20th Advanced Accelerator Concepts Workshop (AAC’22) (2022)

Beamline Design & Simulation (53.2%) Surrogate Models (52.7%) Tools & Libraries (100.0%)

Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment

Sytov, A., Cassou, K., Kubytskyi, V., Lenivenko, M., Huber, A. • arXiv (2025)

Novel Applications (48.7%) Beamline Design & Simulation (46.7%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Machine learning-based particle accelerator modeling

Emmanuel Goutierre • Laboratoire Interdisciplinaire des Sciences du Numérique (2024)

Novel Applications (52.4%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Optimizing Beam-Plasma Interactions Through Jitter Analysis Using Start-to-End Simulations

Hwang, Robin • arXiv (2024)

Novel Applications (54.1%) Beamline Design & Simulation (50.5%) Tools & Libraries (100.0%)

Road map for the tuning of hadronic interaction models with accelerator-based and astroparticle data

Novel Applications (43.8%) Beamline Design & Simulation (40.8%) Tools & Libraries (100.0%)

Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation

Kane, G., Drobniak, P., Kazamias, S., Kubytskyi, V., Lenivenko, M., Lucas, B., Serhal, J., Cassou, K., Beck, A., Specka, A., Massimo, F. • arXiv (2024)

Beamline Design & Simulation (43.0%) Novel Applications (42.4%) Surrogate Models (100.0%)

MiMu: mitigating multiple shortcut learning behavior of transformers

Lili Zhao, Qi Liu, Wei Chen, Liyi Chen, Ruijun Sun, Min Hou, Yang Wang, Shijin Wang, Pingping Ren, Jiafeng Zhou • Frontiers of Computer Science (2025) • DOI: 10.1007/s11704-025-50448-3

Reinforcement Learning & Autonomous Systems (33.7%) Surrogate Models (31.8%)

Method for reconstruction the energy spectrum of a linear electron accelerator using neural networks

A.A. Kim, F.R. Studenikin, V.V. Khankin, S.A. Zolotov, A.P. Lyubomudrov, A.P. Chernyaev • Radiation Physics and Chemistry (2026) • DOI: 10.1016/j.radphyschem.2026.113775

Novel Applications (52.7%) Beamline Design & Simulation (51.7%)

Machine Learning for Optimized Polarization at Jefferson Lab

Novel Applications (35.1%) Statistics & Trends (29.7%) Surrogate Models (100.0%)

Using a neural network model to guide protection heater design in Nb$_{3}$Sn accelerator magnets

Bakrani Balani, Shahriar, Salmi, Tiina • Supercond.Sci.Technol. (2025) • DOI: 10.1088/1361-6668/addaed

Beamline Design & Simulation (32.7%) Statistics & Trends (25.2%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators

Reinforcement Learning & Autonomous Systems (48.7%) Novel Applications (44.8%) Surrogate Models (100.0%)

Ultraefficient Compressed Phonocardiogram Classification on a Custom Embedded Neural Accelerator

Domenico Ragusa, Rens Baeyens, Danilo Pau, Elisa Marenzi, Jan Steckel, Walter Daems, Francesco Leporati, Emanuele Torti • IEEE Internet of Things Journal (2026) • DOI: 10.1109/jiot.2025.3635785

Novel Applications (39.4%) Surrogate Models (35.5%)

Development of an Experimental Test Stand and Machine Learning Surrogate Models for Capturing Complex Responses in Pulsed Septa

Kawa, Krzysztof, Szumlak, Tomasz, Yagci, Omer Yusuf, Balhan, Bruno, Lackner, Friedrich • IEEE Trans.Appl.Supercond. (2026) • DOI: 10.1109/tasc.2026.3660099

Novel Applications (36.1%) Anomaly Detection & Fault Prediction (36.1%) Surrogate Models (100.0%)

Predicting LTS Superconducting NbTi Magnet Quench: A 1D-CNN Model for Real-Time Implementations

Aline Y. Machado, Pedro T. Andrade, Amanda C. Martinez • IEEE Transactions on Applied Superconductivity (2026) • DOI: 10.1109/tasc.2026.3680889

Beamline Design & Simulation (28.2%) RF Systems (28.1%)

femto-PIXAR: a self-supervised neural network method for reconstructing femtosecond X-ray free electron laser pulses

Goetzke, Gesa, Plumley, Rajan, Hartmann, Gregor, Maxwell, Tim, Decker, Franz-Josef, Lutman, Alberto, Dunne, Mike, Ratner, Daniel, Turner, Joshua J. • Opt.Express (2025) • DOI: 10.1364/oe.562798

By Facility Type (28.8%) Surrogate Models (26.4%)

Automated beam tuning of the TRIUMF ISAC facility

Shelbaya, Olivier, Jung, Paul, Kester, Oliver, Hassan, Omar • JACoW (2025) • DOI: 10.18429/jacow-hiat2025-tux02

Novel Applications (48.7%) Beamline Design & Simulation (44.4%) Tools & Libraries (100.0%)

Modelization of an Injector With Machine Learning

Novel Applications (50.6%) Beamline Design & Simulation (50.2%)

Surrogate Model for Linear Accelerator: A fast Neural Network approximation of ThomX's simulator

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag • IPAC 2023 - 14th International Particle Accelerator Conference (2023) • DOI: 10.18429/jacow-ipac2023-thpl039

Novel Applications (57.0%) Beamline Design & Simulation (56.9%) Reviews (100.0%) Surrogate Models (100.0%)

Updates to Xopt for online accelerator optimization and control

Roussel, Ryan, Kennedy, Dylan, Boltz, Tobias, Baker, Kathryn, Mayes, Christopher, Edelen, Auralee • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-thpg85

Tools & Libraries (100.0%) Novel Applications (55.3%) Surrogate Models (100.0%)

High fidelity numerical modelling and condition monitoring applied to septum magnets at CERN

Kawa, Krzysztof, Szumlak, Tomasz, Kokkinos, Charilaos, Lackner, Friedrich • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-thpr29

Beamline Design & Simulation (41.8%) Novel Applications (41.2%) Reviews (100.0%)

Machine learning-based extraction of longitudinal beam parameters in the LHC

Iliakis, Konstantinos, Karlsen-Bæck, Birk Emil, Trad, Georges, Timko, Helga, Zampetakis, Michail, Argyropoulos, Theodoros • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-tups56

Novel Applications (44.7%) Beamline Design & Simulation (43.6%)

Advancements in backwards differentiable beam dynamics simulations for accelerator design, model calibration, and machine learning

Beamline Design & Simulation (54.2%) Novel Applications (53.2%) Tools & Libraries (100.0%)

Advanced algorithms for linear accelerator design and operation

Novel Applications (58.4%) Statistics & Trends (55.5%) Surrogate Models (100.0%)

Machine learning assisted Bayesian calibration of accelerator digital twin from orbit response data

Lin, Weijian, Kelly, Christopher, Hoffstaetter, Georg, Brown, Kevin, Urban, Nathan • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop056

Surrogate Models (55.7%) Novel Applications (44.7%)

Online optimizations of NSLS-II Linac and Linac-to-Booster beam lines using machine learning methods

Song, Minghao, Wang, Guimei, Hidaka, Yoshiteru, Yang, Xi • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop103

Novel Applications (44.7%) Beamline Design & Simulation (42.6%)

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

Pathak, Abhishek, Hanlet, Pierrick, Miceli, Tia • JACoW (2026) • DOI: 10.18429/jacow-napac2025-thp028

Surrogate Models (53.1%) Novel Applications (51.3%) Tools & Libraries (100.0%)

JuTrack, a Julia-based auto-differentiable accelerator simulation code for advanced dynamics, scientific machine learning and optimization

Wan, Jinyu, Alamprese, Helena, Ratcliff, Christian, Qiang, Ji, Hao, Yue • JACoW (2026) • DOI: 10.18429/jacow-napac2025-tup056

Beamline Design & Simulation (55.3%) Novel Applications (53.8%) Tools & Libraries (100.0%)

Signal model parameter scan using Normalizing Flow

Saito, Masahiko, Morinaga, Masahiro, Kishimoto, Tomoe, Tanaka, Junichi • PoS (2024) • DOI: 10.22323/1.458.0017

Surrogate Models (34.0%) Statistics & Trends (31.2%)

How to Overcome Limitations of the Abaqus Superelasticity Material Model with Reduced Order Modeling and Machine Learning

M. Fassin, C. Degel, P. Hempel, K. Schulz • International Conference on Shape Memory and Superelastic Technologies (2026) • DOI: 10.31399/asm.cp.smst2026p0028

Statistics & Trends (25.4%) Beamline Design & Simulation (25.2%)

Assessing the Performance of Deep Learning Predictions for Dynamic Aperture of a Hadron Circular Particle Accelerator

Di Croce, Davide, Giovannozzi, Massimo, Montanari, Carlo Emilio, Pieloni, Tatiana, Redaelli, Stefano, Van der Veken, Frederik F. • Instruments (2024) • DOI: 10.3390/instruments8040050

Novel Applications (49.4%) Tools & Libraries (100.0%) Surrogate Models (100.0%)

Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems

Auralee Edelen, Nicole Neveu, Yannick Huber, Mattias Frey, Christopher Mayes, Andreas Adelmann • arXiv (2020)

Novel Applications (51.5%) Tools & Libraries (100.0%) Surrogate Models (100.0%)

Machine learning for design optimization of storage ring nonlinear dynamics

Faya Wang, Minghao Song, Auralee Edelen, Xiaobiao Huang • arXiv (2019)

Optimization & Control (43.7%) Novel Applications (31.2%) Surrogate Models (100.0%)

Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators

Andrei Ivanov, Ilya Agapov • arXiv (2020)

Novel Applications (49.3%) Beamline Design & Simulation (43.2%)

Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks

J. Ogren, C. Gohil, D. Schulte • arXiv (2021)

Surrogate Models (100.0%) Beamline Design & Simulation (38.0%)

Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster

Reinforcement Learning & Autonomous Systems (58.1%) Novel Applications (55.7%) Surrogate Models (100.0%)

Improving Surrogate Model Accuracy for the LCLS-II Injector Frontend Using Convolutional Neural Networks and Transfer Learning

Lipi Gupta, Auralee Edelen, Nicole Neveu, Aashwin Mishra, Christopher Mayes, Young-Kee Kim • arXiv (2021)

Novel Applications (50.9%) Beamline Design & Simulation (46.4%) Surrogate Models (100.0%)

SPIRAL2 Cryomodules Models: a Gateway to Process Control and Machine Learning

Adrien Vassal, Adnan Ghribi, François Millet, François Bonne, Patrick Bonnay, Pierre-Emmanuel Bernaudin • arXiv (2021)

Beamline Design & Simulation (42.1%) Novel Applications (41.7%)

Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks

Friedrich Bethke, Richard Pausch, Patrick Stiller, Alexander Debus, Michael Bussmann, Nico Hoffmann • arXiv (2021)

Surrogate Models (100.0%) Novel Applications (42.4%)

Fast, efficient and flexible particle accelerator optimisation using densely connected and invertible neural networks

Renato Bellotti, Romana Boiger, Andreas Adelmann • arXiv (2021)

Novel Applications (59.7%) Tools & Libraries (100.0%) Surrogate Models (100.0%)

Input Beam Matching and Beam Dynamics Design Optimization of the IsoDAR RFQ using Statistical and Machine Learning Techniques

Daniel Koser, Loyd Waites, Daniel Winklehner, Matthias Frey, Andreas Adelmann, Janet Conrad • arXiv (2021)

Surrogate Models (100.0%) Beamline Design & Simulation (50.3%) Tools & Libraries (100.0%)

Physics-informed neural network method for modelling beam-wall interactions

Kazuhiro Fujita • arXiv (2022)

Beamline Design & Simulation (52.5%) Novel Applications (49.2%)

Differentiable Preisach Modeling for Characterization and Optimization of Accelerator Systems with Hysteresis

R. Roussel, A. Edelen, D. Ratner, K. Dubey, J. P. Gonzalez-Aguilera, Y. K. Kim, N. Kuklev • arXiv (2022)

Novel Applications (52.2%) Beamline Design & Simulation (51.3%)

Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-based Charged Particle Beams

Auralee Edelen, Christopher Mayes • arXiv (2022)

Novel Applications (52.4%) Beamline Design & Simulation (47.3%)

Predicting Beam Transmission Using 2-Dimensional Phase Space Projections Of Hadron Accelerators

Anthony Tran, Yue Hao, Brahim Mustapha, Jose L. Martinex Marin • arXiv (2022)

Beamline Design & Simulation (40.5%) Novel Applications (37.7%)

Uncertainty Aware ML-based surrogate models for particle accelerators: A Study at the Fermilab Booster Accelerator Complex

Malachi Schram, Kishansingh Rajput, Karthik Somayaji Peng Li, Jason St. John, Himanshu Sharma • arXiv (2022)

Novel Applications (43.2%) Statistics & Trends (43.1%) Surrogate Models (100.0%)

Neural Network Prior Mean for Particle Accelerator Injector Tuning

Connie Xu, Ryan Roussel, Auralee Edelen • arXiv (2022)

Novel Applications (47.5%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Applications of Differentiable Physics Simulations in Particle Accelerator Modeling

Ryan Roussel, Auralee Edelen • arXiv (2022)

Beamline Design & Simulation (57.4%) Novel Applications (54.4%)

Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows

Anna Willmann, Jurjen Couperus Cabadağ, Yen-Yu Chang, Richard Pausch, Amin Ghaith, Alexander Debus, Arie Irman, Michael Bussmann, Ulrich Schramm, Nico Hoffmann • arXiv (2023)

Beam Dynamics (32.2%) Surrogate Models (100.0%)

From Compact Plasma Particle Sources to Advanced Accelerators with Modeling at Exascale

Axel Huebl, Remi Lehe, Edoardo Zoni, Olga Shapoval, Ryan T. Sandberg, Marco Garten, Arianna Formenti, Revathi Jambunathan, Prabhat Kumar, Kevin Gott, Andrew Myers, Weiqun Zhang, Ann Almgren, Chad E. Mitchell, Ji Qiang, David Grote, Alexander Sinn, Severin Diederichs, Maxence Thevenet, Luca Fedeli, Thomas Clark, Neil Zaim, Henri Vincenti, Jean-Luc Vay • arXiv (2023)

Beamline Design & Simulation (53.2%) Surrogate Models (52.7%) Tools & Libraries (100.0%)

Plasma Surrogate Modelling using Fourier Neural Operators

Novel Applications (34.9%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Bayesian Optimization Algorithms for Accelerator Physics

Statistics & Trends (50.2%) Novel Applications (49.8%) Reviews (100.0%) Surrogate Models (100.0%)

Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations

Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia • arXiv (2024)

Novel Applications (56.6%) Reinforcement Learning & Autonomous Systems (53.0%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Abhishek Pathak • arXiv (2024)

Novel Applications (57.2%) Beamline Design & Simulation (50.2%) Tools & Libraries (100.0%)

Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation

G. Kane, P. Drobniak, S. Kazamias, V. Kubytskyi, M. Lenivenko, B. Lucas, J. Serhal, K. Cassou, A. Beck, A. Specka, F. Massimo • arXiv (2025)

Novel Applications (47.8%) Beamline Design & Simulation (46.0%) Surrogate Models (100.0%)

Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators

Kamaljeet Singh, Kangkan Goswami, Raghunath Sahoo, Sumanta Samal • arXiv (2025)

Novel Applications (38.3%) Beamline Design & Simulation (37.1%)

Data-Driven Discovery of Beam Centroid Dynamics

Liam A. Pocher, Irving Haber, Thomas M. Antonsen Jr., Patrick G. O'Shea • arXiv (2024)

Novel Applications (48.3%) Beamline Design & Simulation (45.5%)

Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators

Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch • arXiv (2024)

Reinforcement Learning & Autonomous Systems (46.6%) Novel Applications (43.4%) Surrogate Models (100.0%)

Optimizing Beam-Plasma Interactions Through Jitter Analysis Using Start-to-End Simulations

Robin Hwang • arXiv (2024)

Novel Applications (54.1%) Beamline Design & Simulation (50.5%) Tools & Libraries (100.0%)

Inverse Surrogate Model of a Soft X-Ray Spectrometer using Domain Adaptation

Enrico Ahlers, Peter Feuer-Forson, Gregor Hartmann, Rolf Mitzner, Peter Baumgärtel, Jens Viefhaus • arXiv (2025)

Novel Applications (34.4%) Data Management (29.6%) Surrogate Models (100.0%)

Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment

A. Sytov, K. Cassou, V. Kubytskyi, M. Lenivenko, A. Huber • arXiv (2025)

Novel Applications (48.7%) Beamline Design & Simulation (46.7%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Deep Learning Assisted Modeling for $χ^{(2)}$ Nonlinear Optics

Jack Hirschman, Erfan Abedi, Minyang Wang, Hao Zhang, Abhimanyu Borthakur, Justin Baker, Andrea L. Bertozzi, Randy Lemons, Sergio Carbajo • arXiv (2025)

Surrogate Models (100.0%) Novel Applications (35.0%) Tools & Libraries (100.0%)

A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network

Mianjun Xiao, Peng Song, Yulong Liu, Cedric Korte, Ziyang Xu, Jiale Gao, Jiaqi Lu, Haoyang Nie, Qiantong Deng, Timing Qu • arXiv (2025)

Novel Applications (30.0%) Surrogate Models (100.0%) Tools & Libraries (100.0%)

Acceleration of Multi-Scale LTS Magnet Simulations with Neural Network Surrogate Models

Louis Denis, Julien Dular, Vincent Nuttens, Mariusz Wozniak, Benoît Vanderheyden, Christophe Geuzaine • arXiv (2025)

Surrogate Models (100.0%)

Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models

Guillermo Rodriguez-Llorente, Galo Gallardo, Rodrigo Morant Navascués, Nikita Khvatkin Petrovsky, Anderson Sabogal, Roberto Gómez-Espinosa Martín • arXiv (2025)

Novel Applications (46.0%) Reinforcement Learning & Autonomous Systems (45.9%) Surrogate Models (100.0%)

SE-MLP Model for Predicting Prior Acceleration Features in Penetration Signals

Yankang Li, Changsheng Li • arXiv (2025)

Novel Applications (30.6%) Surrogate Models (30.3%)

U-Net-based surrogate modeling for attosecond X-ray free-electron lasers

Yufei Wei, Bingyang Yan, Chenzhi Xu, Jiawei Yan, Haixiao Deng • arXiv (2026)

Surrogate Models (100.0%) Novel Applications (38.6%)

Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation

Petros Ellinas, Indrajit Chaudhuri, Johanna Vorwerk, Spyros Chatzivasileiadis • arXiv (2026)

Surrogate Models (39.2%) Reinforcement Learning & Autonomous Systems (27.7%) Tools & Libraries (100.0%)

Machine learning-based virtual diagnostics of dielectric laser acceleration

Thilo Egenolf, Oliver Boine-Frankenheim • arXiv (2026)

Surrogate Models (100.0%) Novel Applications (43.3%) Tools & Libraries (100.0%)

Closing the Loop: Deploying Auto-Generating Digital Twins for Particle Accelerators

A. D. Brynes, M. King, K. R. L. Baker, R. Banerjee, R. Clarke, D. J. Dunning, J. K. Jones, M. Leputa, A. E. Pollard, M. Romanovschi, M. Shaw, N. Ziyan • arXiv (2026)

Surrogate Models (67.2%) Beamline Design & Simulation (57.9%)

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