Inertial Confinement Fusion Forecasting via Large Language Models

Controlled fusion energy is deemed pivotal for the advancement of human civilization. In this study, we introduce \(\textbf{LPI-LLM}\), a novel integration of Large Language Models (LLMs) with classical reservoir computing paradigms tailored to address a critical challenge, Laser-Plasma Instabilities (\(\texttt{LPI}\)), in Inertial Confinement Fusion (\(\texttt{ICF}\)). Our approach offers several key contributions: Firstly, we propose the \(\textit{LLM-anchored Reservoir}\), augmented with a \(\textit{Fusion-specific Prompt}\), enabling accurate forecasting of \(\texttt{LPI}\)-generated-hot electron dynamics during implosion. Secondly, we develop \(\textit{Signal-Digesting Channels}\) to temporally and spatially describe the driver laser intensity across time, capturing the unique characteristics of \(\texttt{ICF}\) inputs. Lastly, we design the \(\textit{Confidence Scanner}\) to quantify the confidence level in forecasting, providing valuable insights for domain experts to design the \(\texttt{ICF}\) process. Extensive experiments demonstrate the superior performance of our method, achieving 1.90 CAE, 0.14 \(\texttt{top-1}\) MAE, and 0.11 \(\texttt{top-5}\) MAE in predicting Hard X-ray (\(\texttt{HXR}\)) energies emitted by the hot electrons in \(\texttt{ICF}\) implosions, which presents state-of-the-art comparisons against concurrent best systems. Additionally, we present \(\textbf{LPI4AI}\), the first \(\texttt{LPI}\) benchmark based on physical experiments, aimed at fostering novel ideas in \(\texttt{LPI}\) research and enhancing the utility of LLMs in scientific exploration. Overall, our work strives to forge an innovative synergy between AI and \(\texttt{ICF}\) for advancing fusion energy.