# Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations

**Authors:** Michael D. Vander Wal, Ryan G. McClarren, Kelli D. Humbird  
**arXiv:** [2205.14520](https://arxiv.org/abs/2205.14520)  
**Date:** 2022-05-28  
**Summary page:** https://www.summarizepaper.com/fr/paper/2205.14520v1/

## Abstract

Simulations of high-energy density physics often need non-local thermodynamic equilibrium (NLTE) opacity data. This data, however, is expensive to produce at relatively low-fidelity. It is even more so at high-fidelity such that the opacity calculations can contribute ninety-five percent of the total computation time. This proportion can even reach large proportions. Neural networks can be used to replace the standard calculations of low-fidelity data, and the neural networks can be trained to reproduce artificial, high-fidelity opacity spectra. In this work, it is demonstrated that a novel neural network architecture trained to reproduce high-fidelity krypton spectra through transfer learning can be used in simulations. Further, it is demonstrated that this can be done while achieving a relative percent error of the peak radiative temperature of the hohlraum of approximately 1\% to 4\% while achieving a 19.4x speed up.