455 citations · 930 across the 30 of their papers we have counts for
6 papers · 1 filter
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…
Testing the Reliability of Interpretable Neural Networks in Geoscience Using the Madden-Julian Oscillation
Benjamin A. Toms, Karthik Kashinath, Prabhat +1
We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying th…
Spherical CNNs on Unstructured Grids
Chiyu "Max" Jiang, Jingwei Huang, Karthik Kashinath +3
We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical sig…