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

TL;DR

This paper demonstrates that transfer learning with neural networks can efficiently reproduce high-fidelity NLTE opacity spectra in simulations, significantly reducing computation time while maintaining accuracy.

Contribution

It introduces a novel neural network architecture trained via transfer learning to reproduce high-fidelity krypton spectra in simulations.

Findings

Achieves 1-4% error in peak radiative temperature

Provides a 19.4x speedup in opacity calculations

Successfully applies transfer learning to high-fidelity spectral data

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.