Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization

1 code implementation • 25 Apr 2023 • Adam Rupe, Karthik Kashinath, Nalini Kumar, James P. Crutchfield

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium.

Physics-informed machine learning Representation Learning

DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems

2 code implementations • 25 Sep 2019 • Adam Rupe, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor Lee, Prabhat, James P. Crutchfield

Extracting actionable insight from complex unlabeled scientific data is an open challenge and key to unlocking data-driven discovery in science.

Representation Learning

Towards Unsupervised Segmentation of Extreme Weather Events

no code implementations • 16 Sep 2019 • Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor Lee, Prabhat, James P. Crutchfield

Extreme weather is one of the main mechanisms through which climate change will directly impact human society.

Representation Learning

CosmoFlow: Using Deep Learning to Learn the Universe at Scale

no code implementations • 14 Aug 2018 • Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee

Deep learning is a promising tool to determine the physical model that describes our universe.