16 citations · 16 across the 1 of their papers we have counts for
2 papers
physics.comp-ph2019★ 16 cited
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Liu Yang, Sean Treichler, Thorsten Kurth +8
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…
cs.DC2018
Exascale Deep Learning for Climate Analytics
Thorsten Kurth, Sean Treichler, Joshua Romero +9
We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipel…