Asset Details
Do you wish to reserve the book?
Inertial Confinement Fusion Forecasting via Large Language Models
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Inertial Confinement Fusion Forecasting via Large Language Models
Do you wish to request the book?
Inertial Confinement Fusion Forecasting via Large Language Models
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Paper
Inertial Confinement Fusion Forecasting via Large Language Models
2024
Overview
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.
Publisher
Cornell University Library, arXiv.org
