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

arXiv: https://arxiv.org/abs/2106.02528 · HTML: https://mlsanity.com/paper/2106.02528/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks](https://mlsanity.com/paper/1907.04595/) (2019)
- [Transfer learning to model inertial confinement fusion experiments](https://mlsanity.com/paper/1812.06055/) (2018)

## Built on by (most cited first, 2 of 2)

- [Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models](https://mlsanity.com/paper/2203.00853/) (2022)
- [Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations](https://mlsanity.com/paper/2205.14520/) (2022)

## Abstract (arXiv)

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.