The AI/ML for particle accelerators living review

LIVING REVIEW • CONTINUOUSLY UPDATED 🕒 Last update: May 30, 2026, 06:40 UTC 🔮 Next update: May 31, 2026, 06:40 UTC
534+
Papers Reviewed
16
Categories
May 2026
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 May 2026

A complete discussion on fully reconfigurable, digital, scalable, graph and sparsity-aware near-memory accelerator for graph neural networks
Siddhartha Raman Sundara Raman, LIZY JOHN, Jaydeep P. Kulkarni • ArXiv.org (2026)
Advanced Machine Learning Enabled Modern Power System
Muhammad Asim Amin • CINECA IRIS Institutial Research Information System (University of Genoa) (2026)
Machine Learning-Based Graph Simplification for Symbolic Accelerators
Tiffany Yu, Rye Stahle-Smith, Darssan Eswaramoorthi, et al. • ArXiv.org (2026)
Multi-class anatomical landmark detection in periapical radiographs with deep learning
Cansu Buyuk, Alperen Saruhan, Fatma Yuce, et al. • Odontology (2026)
Comparative efficacy of supervised and semi-supervised machine learning classifiers for energy-based discrimination of near-fault pulse-type ground motion
Sayed S. R. Moustafa, M. Sami Soliman, Shimaa H. Elkhouly, et al. • Earth Science Informatics (2026)
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