On the design and evaluation of generative models in high energy density physics

On the design and evaluation of generative models in high energy density physics

Abstract Understanding high energy density physics (HEDP) is critical for advancements in fusion energy and astrophysics. The computational demands of the computer models used for HEDP studies have led researchers to explore deep learning methods to enhance simulation efficiency. This paper introduc...

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Main Authors: Ankita Shukla, Yamen Mubarka, Rushil Anirudh, Eugene Kur, Derek Mariscal, Blagoje Djordjevic, Bogdan Kustowski, Kelly Swanson, Brian Spears, Peer-Timo Bremer, Tammy Ma, Pavan Turaga, Jayaraman J. Thiagarajan
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Communications Physics
Online Access:https://doi.org/10.1038/s42005-024-01912-2
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https://doi.org/10.1038/s42005-024-01912-2

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