Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

Code

• github.com/rushilanirudh/icf-jag-cycleGAN
  tf★ 30
• github.com/rushilanirudh/macc
  tf★ 0

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.

Reproductions