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

Simulations of high energy density physics are expensive in terms of computational resources. In particular, the computation of opacities of plasmas, which are needed to accurately compute radiation transport in the non-local thermal equilibrium (NLTE) regime, are expensive to the point of easily requiring multiple times the sum-total compute time of all other components of the simulation. As such, there is great interest in finding ways to accelerate NLTE computations. 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 multiple elements in demonstrating that individual surrogate models can be also be generated for other elements with the focus being on creating autoencoders that can accurately encode and decode the absorptivity and emissivity spectra. Furthermore, this work shows that multiple elements across a large range of atomic numbers can be combined into a single autoencoder when using a convolutional autoencoder while maintaining accuracy that is comparable to individual fully-connected autoencoders. Lastly, it is demonstrated that DJINN can effectively learn the latent space of a convolutional autoencoder that can encode multiple elements allowing the combination to effectively function as a surrogate model.