The AI/ML for particle accelerators living review

LIVING REVIEW • CONTINUOUSLY UPDATED 🕒 Last update: March 15, 2026, 05:11 UTC 🔮 Next update: March 16, 2026, 05:11 UTC
460+
Papers Reviewed
16
Categories
Mar 2026
Last Updated
23
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 March 2026

Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators
Hemkant Nehete, Gaurav Verma, Amit Monga, et al. • Unknown Venue (2026)
Temporal-frequency enhanced fourier neural operator and collaborative adversarial learning for multi-physics-coupled transient simulation of natural gas pipelines
Yuqian Wang, Gang Liu, Xiaojing Zhu, et al. • Expert Systems with Applications (2026)
X-ray CT analysis of internal defects in 3D-printed continuous carbon fiber composites using deep learning
Runze Yang, Jinyue Zhang • Materials Letters (2026)
Method for reconstruction the energy spectrum of a linear electron accelerator using neural networks
A.A. Kim, F.R. Studenikin, V.V. Khankin, et al. • Radiation Physics and Chemistry (2026)
Optimizing pulsed field ablation for cardiac arrhythmias integrating Taguchi method, machine learning and genetic algorithms
Ethan Nabuurs, Kuljeet Singh Grewal, Amara Sanchez, et al. • Results in Engineering (2026)
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