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

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...

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Main Authors: Min Sang Cho, Paul E. Grabowski, Kowshik Thopalli, Thathachar S. Jayram, Michael J. Barrow, Jayaraman J. Thiagarajan, Rushil Anirudh, Hai P. Le, Howard A. Scott, Joshua B. Kallman, Branson C. Stephens, Mark E. Foord, Jim A. Gaffney, Peer-Timo Bremer
Format: Article
Language:English
Published: American Physical Society 2025-05-01
Series:Physical Review Research
Online Access:http://doi.org/10.1103/PhysRevResearch.7.023150
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http://doi.org/10.1103/PhysRevResearch.7.023150

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