Machine Learning in Inertial Confinement Fusion: Applications, Challenges, and Prospects

Abstract

The recent demonstration of laser-driven fusion ignition at the National Ignition Facility (NIF) has marked a pivotal advancement in inertial confinement fusion (ICF) research. This approach relies on precisely controlled high-power laser facilities to compress and heat fusion fuel to extreme conditions sufficient for nuclear fusion. Achieving optimal target performance presents challenges in laser control and management, experimental design, and physical modeling, compounded by the substantial costs of experiments and numerical simulations. Recently, machine learning (ML) has been widely applied in ICF research due to its enhanced computational efficiency and data-driven modeling capabilities. This paper provides a review of ML applications in ICF, addressing current advancements, challenges, and prospects. First, it outlines the key technical bottlenecks in ICF and discusses how ML approaches offer potential solutions. It then describes the implementation of major ML algorithms across ICF research and systematically reviews recent advances in laser control and optimization, operation and management of high-power laser facilities, and optimization and design of ICF physics experiments. Subsequently, it provides a detailed analysis of ML applications to five tasks: image analysis, data processing, optimization and design, intelligent and precision control, and prediction and inversion. Finally, open challenges, potential solutions, and future perspectives are discussed.