The AI/ML for particle accelerators living review

LIVING REVIEW • CONTINUOUSLY UPDATED 🕒 Last update: February 28, 2026, 04:21 UTC 🔮 Next update: March 1, 2026, 04:21 UTC
436+
Papers Reviewed
16
Categories
Feb 2026
Last Updated
27
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 February 2026

High-Resolution Image Reconstruction with Unsupervised Learning and Noisy Data Applied to Ion-Beam Dynamics for Particle Accelerators
Francis Osswald, Mohammed Chahbaoui, X. Liang • Journal of Instrumentation (2026)
Development and implementation of the IMRA multiplatform Foundations in Robotic Surgery online learning curriculum
Tayla Fay, Daniel Costello, Dean Driscoll, et al. • Journal of Robotic Surgery (2026)
Advanced quality assessment and damage monitoring in 3D-printed plastic and composite beams using machine learning
Gia Hoang Phan • Progress in Additive Manufacturing (2026)
Optimization of the Claus Process Using Machine Learning and Real Operational Data: A Case Study in an SRU
Amirfarzam Shokouhalaei • Chemistry Africa (2026)
Toward closed-loop quality assurance in powder bed fusion additive manufacturing: Defect detection, machine learning, and computational modeling
Mohammad Taghian, Ali Pilehvar Meibody, Abdollah Saboori, et al. • Journal of Manufacturing Processes (2026)
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