Towards Using Large Language Models and Deep Reinforcement Learning for Inertial Fusion Energy

Abstract

Fusion energy research has long captured the public imagination for its applications to fundamental physics, material sciences, and as a low-carbon-footprint electrical power source. The National Ignition Facility (NIF) recently demonstrated that focusing lasers onto a very small target of hydrogen isotopes can produce conditions for nuclear fusion. Despite such remarkable progress, sustainable production of inertial fusion energy (IFE) still presents a huge challenge due to a vast space of parameters that must be explored in order to find optimum conditions for a thermonuclear ignition. It is perceived that artificial intelligence (AI) can pla a crucial role in advancing IFE technology. We present our vision of how large language models (LLM) and deep reinforcement learning (DRL) can guide IFEresearch.