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

Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …

Online tuning of particle accelerators is a complex optimisation problem that continues to
require manual intervention by experienced human operators. Autonomous tuning is a …

High-fidelity physics simulations are powerful tools in the design and optimization of
charged particle accelerators. However, the computational burden of these simulations often …

The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …

We report on the application of machine learning (ML) methods for predicting the
longitudinal phase space (LPS) distribution of particle accelerators. Our approach consists …

Extremum seeking (ES) is a powerful approach to the optimization and stabilization of
unknown dynamic systems and is an active field of research in control theory. This paper …

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 …

We describe a method for precisely regulating the gradient magnet power supply (GMPS) at
the Fermilab Booster accelerator complex using a neural network trained via reinforcement …