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