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20182020
most citedSpatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks

7 citations · 8 across the 2 of their papers we have counts for

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physics.comp-ph2019

Towards Physics-informed Deep Learning for Turbulent Flow Prediction

Rui Wang, Karthik Kashinath, Mustafa Mustafa +2

While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, t…

physics.comp-ph2019

Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems

Jin-Long Wu, Karthik Kashinath, Adrian Albert +3

Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully…