# Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data

arXiv: https://arxiv.org/abs/2103.10590 · HTML: https://mlsanity.com/paper/2103.10590/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 19 Mar 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Transfer learning to model inertial confinement fusion experiments](https://mlsanity.com/paper/1812.06055/) (2018)

## Built on by (most cited first, 6 of 6)

- [Transfer learning driven design optimization for inertial confinement fusion](https://mlsanity.com/paper/2205.13519/) (2022)
- [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)
- [Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion](https://mlsanity.com/paper/2607.10366/) (2026)
- [Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction](https://mlsanity.com/paper/2609.07756/) (2026)

## Abstract (arXiv)

The design space for inertial confinement fusion (ICF) experiments is vast and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. However, ICF multiphysics codes must make simplifying assumptions, and thus deviate from experimental measurements for complex implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. In this work, we describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model. This method leverages a machine learning technique called transfer learning, the process of taking a model trained to solve one task, and partially retraining it on a sparse dataset to solve a different, but related task. In the context of ICF design, neural network models trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone. We demonstrate improved model performance for a range of ICF experiments at the National Ignition Facility, and predict the outcome of recent experiments with less than ten percent error for several key observables. We discuss how the methods might be used to carry out a data-driven experimental campaign to optimize performance, illustrating the key product -- models that become increasingly accurate as data is acquired.