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

Published: Oct 3, 2019
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...
Paper Details
Title
Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion.
Published Date
Oct 3, 2019
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