# Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models

arXiv: https://arxiv.org/abs/2203.00853 · HTML: https://mlsanity.com/paper/2203.00853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Transfer learning enhanced physics informed neural network for phase-field modeling of fracture](https://mlsanity.com/paper/1907.02531/) (2019)
- [Transfer learning to model inertial confinement fusion experiments](https://mlsanity.com/paper/1812.06055/) (2018)
- [Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data](https://mlsanity.com/paper/2103.10590/) (2021)
- [Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements](https://mlsanity.com/paper/2106.02528/) (2021)

## Built on by (most cited first, 1 of 1)

- [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, largely in part for the need to produce non-local thermodynamic equilibrium opacities. High-fidelity spectra may reveal new physics in the simulations not seen with low-fidelity spectra, but the cost of these simulations also scale with the level of fidelity of the opacities being used. Neural networks are capable of reproducing these spectra, but neural networks need data to to train them which limits the level of fidelity of the training data. This paper demonstrates that it is possible to reproduce high-fidelity spectra with median errors in the realm of 3% to 4% using as few as 50 samples of high-fidelity Krypton data by performing transfer learning on a neural network trained on many times more low-fidelity data.