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.
@article{arxiv.2106.02528,
title = {Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements},
author = {Michael D. Vander Wal and Ryan G. McClarren and Kelli D. Humbird},
journal= {arXiv preprint arXiv:2106.02528},
year = {2021}
}
Comments
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