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arXiv · 2106.02528

Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements

Michael D. Vander Wal·Ryan G. McClarren·Kelli D. Humbird
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Abstract

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.

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Michael D. Vander Wal, Ryan G. McClarren, Kelli D. Humbird. 2021-06-04. Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements. https://arxiv.org/abs/2106.02528

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