Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

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.

Machine Learning for Physical Sciences Workshop at NeurIPS 2019

References in corpus (2)

• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data