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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%)

Achievement of High‐Quality Gallium Oxide Epitaxial Growth via Machine Learning

Yaoping Lu, Yu Zhang, Ben Niu, Titao Li, Zhenjie Zheng, Lemin Jia, Duanyang Chen, Hongji Qi, Kelvin H. L. Zhang, Min Zhu, Haizhong Zhang, Xiaoqiang Lu • Advanced Functional Materials (2025) • DOI: 10.1002/adfm.202519854

Tools & Libraries (26.8%) Statistics & Trends (26.5%)

A “Bottom-Up” Approach for Constructing Underground Engineering Knowledge Graph

Yiming Luo, Yunfei Xiang, Peng Lin, Yong Xia, Yuanguang Liu, Yao Xu, Chaoyi Li • Mechanisms and Machine Science (2025) • DOI: 10.1007/978-3-031-96732-0_12

Others

Deep Learning-Based Classification of Spine X-Ray Images Using Attention Mechanisms

Asaram Pandurang Janwale, Minal Dutta, Savita Mohurle, Vaduguru Venkata Ramya • Lecture Notes in Networks and Systems (2025) • DOI: 10.1007/978-3-032-06671-8_2

Others

Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [$$^{18}$$F]FDG PET Images

Christian Salomonsen, Kristoffer K. Wickstrøm, Samuel Kuttner, Elisabeth Wetzer • Lecture Notes in Computer Science (2025) • DOI: 10.1007/978-3-032-07904-6_17

Others

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%)

PSVM-MR: A Parallel Support Vector Machine Algorithm Based on MapReduce

Bin-bin Guo, Yimin Mao, A Yaser, Neelakandan Chandrasekaran, Le Kang, Wenhao Li, Decheng Miao • Communications in Computer and Information Science (2025) • DOI: 10.1007/978-981-95-3643-6_6

Data Management (34.8%) Statistics & Trends (26.6%)

Robust Training to Secure Automated AI Accelerator Generation Against Malicious Platforms

Chao Guo, Youhua Shi • Communications in Computer and Information Science (2025) • DOI: 10.1007/978-981-95-4109-6_6

Tools & Libraries (51.9%) Reinforcement Learning & Autonomous Systems (51.2%)

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%)

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%)

Machine-Learning Approach for Pulsed Electromagnetic Field Therapy Parameters Optimization for Enhanced Tissue Penetration

Pooja Kumari Jha, Vikas Kumar, Manoj Kumar Parida, S. Kanagaraj • Lecture Notes in Networks and Systems (2025) • DOI: 10.1007/978-981-96-7614-9_2

Others

Saffron Adulteration Prediction Based on Fine-Grained Deep Features

Ishrat Nazeer, Ranjeet Kumar Rout, Saiyed Umer • Lecture Notes in Networks and Systems (2025) • DOI: 10.1007/978-981-96-7614-9_33

Others

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%)

Railway Track Obstacle Detection and Alert System Using Deep Learning

R. Anand, D. P. Monish, D. Madhukar, C. G. Paari • Lecture Notes in Networks and Systems (2025) • DOI: 10.1007/978-981-96-8796-1_15

Reinforcement Learning & Autonomous Systems (25.0%)

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%)

Enhancing Predictive Accuracy in Shear Strength of RC Deep Beams: A Comprehensive Analysis Using Ensemble Machine Learning Models

Arslan Qayyum Khan, Muhammad Huzaifa Naveed, Muhammad Dawood Rasheed, Amorn Pimanmas • Arabian Journal for Science and Engineering (2025) • DOI: 10.1007/s13369-025-10763-0

Statistics & Trends (28.4%) Tools & Libraries (25.8%)

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%)

Human-in-the-loop reinforcement learning for data quality monitoring in particle physics experiments

Olivia Jullian Parra, Julián García Pardiñas, Lorenzo Del Pianta Pérez, Maximilian Janisch, Suzanne Klaver, Thomas Lehéricy, Nicola Serra • Machine Learning: Science and Technology (2025) • DOI: 10.1088/2632-2153/ae1563

Reinforcement Learning & Autonomous Systems (49.7%) Data Management (49.1%) Tools & Libraries (100.0%)

A data-driven hybrid method combining experiments, finite element modeling and machine learning for impact response prediction of TPU composites

Shunqi Zhang, Luca Lomazzi, Dayou Ma, Andrea Manes • International Journal of Structural Integrity (2025) • DOI: 10.1108/ijsi-07-2025-0180

Tools & Libraries (100.0%)

Machine learning-driven optimization of energy absorption in PLA composites reinforced with carbon fibers through FFF additive manufacturing

Rezgar Hasanzadeh • Rapid Prototyping Journal (2025) • DOI: 10.1108/rpj-06-2025-0219

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%)

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%)

Two-Step Deep Learning Approach for Fine-Grained Railway Catenary Segmentation

Milo Beliën, Metehan Doyran • Proceedings of the International Workshop on Application-driven Point Cloud Processing and 3D Vision (2025) • DOI: 10.1145/3728486.3759209

Others

ChatEED: An agentic retrieval assistant for accelerator operators

Aaron Zachary Reed, Claudio Bisegni, Sandesh Shrestha, Michelle Huang, Daniel Ratner • Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (2025) • DOI: 10.1145/3731599.3767408

Data Management (53.0%) Tools & Libraries (52.7%)

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%)

Abstract 4345868: Machine learning prediction of myocardial ischemia using quantitative assessment of coronary calcifications in non-contrast CT calcium scoring scans

Juhwan Lee, Mohamed H.E. Makhlouf, Sadeer Al-Kindi, Ammar Hoori, Tao Hu, Hao Wu, Justin Kim, Sanjay Rajagopalan, David Wilson • Circulation (2025) • DOI: 10.1161/circ.152.suppl_3.4345868

Others

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%)

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%)

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%)

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%)

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%)

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%)

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%)

Collective User Behavior Driven Self Learning Search Engine

Zhiar Piroti • Springer Science and Business Media LLC (2025) • DOI: 10.21203/rs.3.rs-7784420/v1

Tools & Libraries (100.0%)

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%)

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%)

Machine learning with ultrasound examination for prediction of intraoperative hypotension during robot-assisted radical prostatectomy

V. S. Andreenkov, A. V. Vlasenko, A. N. Kornienko, A. S. Kazakov, E. P. Rodionov, K. B. Kolontarev, E. A. Evdokimov • Medical alphabet (2025) • DOI: 10.33667/2078-5631-2025-27-30-38

Others

Fusion model combining ultrasound-based radiomics and deep transfer learning with clinical parameters for preoperative prediction of pelvic lymph node metastasis in cervical cancer

Jihan Wang, Shengxian Bao, Tongtong Huang, Yongzhi Cai, Binbin Jin, Ji Wu • Frontiers in Oncology (2025) • DOI: 10.3389/fonc.2025.1681029

Tools & Libraries (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%)

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%)

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%)

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%)

Fluence Map Prediction with Deep Learning: A Transformer-based Approach

Ujunwa Mgboh, Rafi Sultan, Dongxiao Zhu, Joshua Kim • arXiv (2025)

Data Management (29.4%) Tools & Libraries (100.0%)

📊 Publication Statistics & Trends

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Publication Trend Over Time

Papers by Category

Recent Activity (Last 6 Months)

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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%)

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%)

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%)

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%)

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%)

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%)

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%)

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%)

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 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%)

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%)

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%)

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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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%)

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%)

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%)

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%)

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%)

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%)

Novel Applications

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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%)

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%)

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%)

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 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 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%)

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%)

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%)

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 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%)

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%)

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%)

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%)

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%)

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%)

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%)

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%)

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%)

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

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

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%)

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%)

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%)

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%)

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%)

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

▼

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%)

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%)

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%)

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%)

Simulations

▼

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%)

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%)

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%)

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%)

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%)

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