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

25 Sep 2019Adam RupeNalini KumarVladislav EpifanovKarthik KashinathOleksandr PavlykFrank SchlimbachMostofa PatwarySergey MaidanovVictor LeePrabhatJames P. Crutchfield

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 supervised machine learning approaches, unsupervised physics-based methods based on behavior-driven theories hold great promise... (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet