# Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

arXiv: https://arxiv.org/abs/1910.01666 · HTML: https://mlsanity.com/paper/1910.01666/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

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

- [PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion](https://mlsanity.com/paper/2603.04606/) (2026)

## Abstract (arXiv)

There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion. However, a common challenge is to verify the scientific plausibility or validity of outputs predicted by a neural network. This work advocates the use of known scientific constraints as a lens into evaluating, exploring, and understanding such predictions for the problem of inertial confinement fusion.