2021/06/04 by Michael D. Vander Wal, Wal, Michael D. Vander, Ryan G. McClarren +4
Computer Science · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Magnetic confinement fusion research #Nuclear Physics and Applications #Nuclear reactor physics and engineering #Plasma Physics (physics.plasm-ph) #cs.LG #physics.plasm-ph
paper · pdf · doi:10.48550/arxiv.2106.02528
Elsevier Review Format, Double Spaced, 26 pages, 10 figures, 5 tables Michael D. Vander Wal: conceptualization, investigation, writing - original draft, writing - editing and review. Ryan G. McClarren - conceptualization, writing - editing and review. Kelli D. Humbird: conceptualization, writing - editing and review
openalex publication_date 2021/06/04 · arxiv created 2021/10/11 · arxiv updated 2021/10/12 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
Simulations of high energy density physics are expensive in terms of computational resources. In particular, the computation of opacities of plasmas in the non-local thermal equilibrium (NLTE) regime can consume as much as 90% of the total computational time of radiation hydrodynamics simulations for high energy density physics applications. Previous work has demonstrated that a combination of fully-connected autoencoders and a deep jointly-informed neural network (DJINN) can successfully replace the standard NLTE calculations for the opacity of krypton. This work expands this idea to combining multiple elements into a single surrogate model with the focus here being on the autoencoder.