Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
Computational Physics
2019-10-07 v1 Computer Vision and Pattern Recognition
Machine Learning
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1910.01666,
title = {Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion},
author = {Rushil Anirudh and Jayaraman J. Thiagarajan and Shusen Liu and Peer-Timo Bremer and Brian K. Spears},
journal= {arXiv preprint arXiv:1910.01666},
year = {2019}
}
Comments
Machine Learning for Physical Sciences Workshop at NeurIPS 2019