Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Published: Oct 29, 2019
Abstract
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site require training a...
Paper Details
Title
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Published Date
Oct 29, 2019
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