Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations
This addresses the bottleneck of expensive opacity calculations in simulations for researchers in high-energy density physics, representing an incremental improvement with specific gains.
The paper tackled the high computational cost of generating high-fidelity NLTE opacity data for high-energy density physics simulations by using a novel neural network architecture with transfer learning to reproduce such data, achieving a 19.4x speed-up and 1-4% relative error in peak radiative temperature.
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.