Machine Learning for Design and Control of Particle Accelerators: A Look Backward and Forward

Abstract

Particle accelerators are extremely complex machines that are challenging to simulate, design, and control. Over the past decade, artificial intelligence (AI) and machine learning (ML) techniques have made dramatic advancements across various scientific and industrial domains, and rapid improvements have been made in the availability and power of computing resources. These developments have begun to revolutionize the way particle accelerators are designed and controlled, and AI/ML techniques are beginning to be incorporated into regular operations for accelerators. This article provides a high-level overview of the history of AI/ML in accelerators and highlights current developments along with contrasting discussion about traditional methods for accelerator design and control. Areas of current technological challenges in developing reliable AI/ML methods are also discussed along with future research directions.