455 citations · 930 across the 30 of their papers we have counts for
4 papers · 2 filters
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…
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems
Adam Rupe, Nalini Kumar, Vladislav Epifanov +8
Extracting actionable insight from complex unlabeled scientific data is an open challenge and key to unlocking data-driven discovery in science. Complementary and alternative to su…
Towards Unsupervised Segmentation of Extreme Weather Events
Adam Rupe, Karthik Kashinath, Nalini Kumar +3
Extreme weather is one of the main mechanisms through which climate change will directly impact human society. Coping with such change as a global community requires markedly impro…
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…