Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Particle accelerators are complex facilities that produce large amounts of structured data
and have clear optimization goals as well as precisely defined control requirements. As such …

High-power particle accelerators are complex machines with thousands of pieces of
equipment that are frequently running at the cutting edge of technology. In order to improve …

This review represents a summary of recent work in the application of artificial intelligence
(AI) and machine learning (ML) in nuclear science, covering topics in nuclear theory …

Accelerators produce too many signals for a small operations team to monitor in real time. In
addition, many of these signals are only interpretable by subject matter experts with years of …

Standard deep learning methods, such as Ensemble Models, Bayesian Neural Networks,
and Quantile Regression Models provide estimates of prediction uncertainties for data …

Particle accelerators are complex and comprise thousands of components, with many
pieces of equipment running at their peak power. Consequently, they can fault and abort …

This paper presents an in-depth analysis of multi-modal, deep learning-based frameworks
for fault detection within big research infrastructures, with a specific focus on synchrotron …

High energy particle accelerators rely on superconducting radio frequency cavities to
transfer energy and accelerate the beam. Such particle accelerators are complex and …

Particle accelerators are complex and comprise thousands of components, with many
pieces of equipment running at their peak power. Consequently, particle accelerators can …