20 citations · 69 across the 7 of their papers we have counts for
Showing physics.comp-phShow all
2 papers · 1 filter
physics.comp-ph2020★ 20 cited
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath +3
Simulation of turbulent flows at high Reynolds number is a computationally challenging task relevant to a large number of engineering and scientific applications in diverse fields…
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…