Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations

The integration of machine-learning techniques into inertial confinement fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly nonlocal thermodynamic equilibrium (NLTE) model with machine-learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine-learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. Thus, we propose novel physics-informed transformations designed to emphasize energy transport, use these transformations to establish new error metrics, and demonstrate that they yield smaller errors within reduced principal-component spaces compared to conventional transformations.

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Publication Details

Journal
Physical Review Research
Published
2025-05-15
DOI
https://doi.org/10.1103/physrevresearch.7.023150
Citations
2
Primary Topic
Laser-Plasma Interactions and Diagnostics
Type
article
Field-Weighted Citation Impact
3.74

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article

Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations

H. A. Scott, Peer‐Timo Bremer, T. S. Jayram, Mark Foord et al.
2 citations
Physical Review Research
Laser-Plasma Interactions and Diagnostics
3.74
article

Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations

H. A. Scott, Peer‐Timo Bremer, T. S. Jayram, Mark Foord, Rushil Anirudh, Hai Le, Jayaraman J. Thiagarajan, J. Kallman, Jim Gaffney, Min Sang Cho, Paul Grabowski, Kowshik Thopalli, Michael Barrow, Branson C. Stephens
article en
2 citations

Abstract

The integration of machine-learning techniques into inertial confinement fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly nonlocal thermodynamic equilibrium (NLTE) model with machine-learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine-learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. Thus, we propose novel physics-informed transformations designed to emphasize energy transport, use these transformations to establish new error metrics, and demonstrate that they yield smaller errors within reduced principal-component spaces compared to conventional transformations.

Physical Review ResearchVol. 7(2)
Lawrence Livermore National Laboratory (US), Stanford University (US)
U.S. Department of Energy, Laboratory Directed Research and Development, Lawrence Livermore National Laboratory
Openalex Percentile: Top 8%
Laser-Plasma Interactions and Diagnostics
3.74
DOIOpen Access PDFOpenAlex
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