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

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

Particle accelerators are extremely complex machines that are challenging to simulate,
design, and control. Over the past decade, artificial intelligence (AI) and machine learning …

Abstract Machine learning (ML) tools such as encoder-decoder convolutional neural
networks (CNN) can represent incredibly complex nonlinear functions which map between …

Following the spectacular success of the third-generation light source over the past
decades, a few new-generation light sources based on the multibend achromat (MBA) …

Machine learning (ML) is growing in popularity for various particle accelerator applications
including anomaly detection such as faulty beam position monitor or RF fault identification …

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 …

Neural network (NN) has been tentatively combined into multi-objective genetic algorithms
(MOGAs) to solve the optimization problems in physics. However, the computationally …

To improve the performance-critical stability and brightness of the electron bunch at injection
into the proton-driven plasma wakefield at the AWAKE CERN experiment, automation …

Fourth-generation storage rings enabled by multi-bend achromat lattices are being
inaugurated worldwide and many more are planned for the next decade. These sources …