# Transfer learning to model inertial confinement fusion experiments

arXiv: https://arxiv.org/abs/1812.06055 · HTML: https://mlsanity.com/paper/1812.06055/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2018.

## Built on by (most cited first, 14 of 14)

- [Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data](https://mlsanity.com/paper/2103.10590/) (2021)
- [Transfer learning driven design optimization for inertial confinement fusion](https://mlsanity.com/paper/2205.13519/) (2022)
- [Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision](https://mlsanity.com/paper/2402.03480/) (2024)
- [Data-driven Science and Machine Learning Methods in Laser-Plasma Physics](https://mlsanity.com/paper/2212.00026/) (2022)
- [Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements](https://mlsanity.com/paper/2106.02528/) (2021)
- [Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data](https://mlsanity.com/paper/2312.03642/) (2023)
- [Suppressing simulation bias using multi-modal data](https://mlsanity.com/paper/2104.09684/) (2021)
- [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)
- [Causal Multi-fidelity Surrogate Forward and Inverse Models for ICF Implosions](https://mlsanity.com/paper/2509.05510/) (2025)
- [Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy](https://mlsanity.com/paper/2510.17830/) (2025)
- [Sim+Real: Joint Simulation-Experiment Training Improves Balanced Prediction in Physical Systems](https://mlsanity.com/paper/2610.01974/) (2026)
- [Transfer learning for predicting source terms of principal component transport in chemically reactive flow](https://mlsanity.com/paper/2312.00356/) (2023)
- [Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction](https://mlsanity.com/paper/2609.07756/) (2026)

## Abstract (arXiv)

Inertial confinement fusion (ICF) experiments are designed using computer simulations that are approximations of reality, and therefore must be calibrated to accurately predict experimental observations. In this work, we propose a novel nonlinear technique for calibrating from simulations to experiments, or from low fidelity simulations to high fidelity simulations, via "transfer learning". Transfer learning is a commonly used technique in the machine learning community, in which models trained on one task are partially retrained to solve a separate, but related task, for which there is a limited quantity of data. We introduce the idea of hierarchical transfer learning, in which neural networks trained on low fidelity models are calibrated to high fidelity models, then to experimental data. This technique essentially bootstraps the calibration process, enabling the creation of models which predict high fidelity simulations or experiments with minimal computational cost. We apply this technique to a database of ICF simulations and experiments carried out at the Omega laser facility. Transfer learning with deep neural networks enables the creation of models that are more predictive of Omega experiments than simulations alone. The calibrated models accurately predict future Omega experiments, and are used to search for new, optimal implosion designs.