The AI/ML for particle accelerators living review

LIVING REVIEW • CONTINUOUSLY UPDATED 🕒 Last update: November 17, 2025, 03:55 UTC 🔮 Next update: November 18, 2025, 03:55 UTC
303+
Papers Reviewed
16
Categories
Nov 2025
Last Updated
28
Papers This Month

Overview

This living review provides a comprehensive survey of artificial intelligence and machine learning applications in particle accelerator science. As the field rapidly evolves, we continuously update this review to reflect the latest research, methodologies, and experimental results from facilities worldwide. Our goal is to serve as a central reference for researchers, operators, and engineers working at the intersection of AI/ML and accelerator physics.

Latest Additions

Recently added papers from November 2025

Deep Learning-Based Classification of Spine X-Ray Images Using Attention Mechanisms
Asaram Pandurang Janwale, Minal Dutta, Savita Mohurle, et al. • Lecture Notes in Networks and Systems (2025)
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, et al. • Lecture Notes in Computer Science (2025)
NNia-8: An 8-Core RISC-V Neural Network Inference Accelerator with Efficient Processing Elements and Memory Utilization
Xingbo Wang, Yucong Huang, Xinyu Kang, et al. • Lecture Notes in Computer Science (2025)
Robust Training to Secure Automated AI Accelerator Generation Against Malicious Platforms
Chao Guo, Youhua Shi • Communications in Computer and Information Science (2025)
Predictive Modeling of ICU Mortality Using Supervised Machine Learning Algorithms
Malithi Upeksha, Sulanie Perera • Communications in Computer and Information Science (2025)
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