The AI/ML for particle accelerators living review

LIVING REVIEW • CONTINUOUSLY UPDATED 🕒 Last update: April 14, 2026, 05:28 UTC 🔮 Next update: April 15, 2026, 05:28 UTC
486+
Papers Reviewed
16
Categories
Apr 2026
Last Updated
11
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 April 2026

Deep learning for carotid Doppler spectra classification
Charita Bhikha, Kahesh Dhuness, Mathilda Mennen, et al. • Medical & Biological Engineering & Computing (2026)
Publisher Correction: Fine-Tuning Universal Machine-Learned Interatomic Potentials for Applications in the Science of Steels
Naveen K. Mohandas, Sebastián Echeverri Restrepo, Marcel H. F. Sluiter • Journal of Phase Equilibria and Diffusion (2026)
Reduced dataset-based machine learning model for blast damage assessment of reinforced concrete columns
Yeeun Kim, Kihak Lee, Jiuk Shin • Engineering Structures (2026)
Deep learning-based high-speed railway communication systems
Do Viet Ha, Trinh Van Chien, Hien Quoc Ngo • Scientific Reports (2026)
Microsecond-latency feedback at a particle accelerator by online reinforcement learning on hardware
L. Scomparin, Michele Caselle, Andrea Santamaria Garcia, et al. • Machine Learning Science and Technology (2026)
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