A Physics-Based Approach to Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems

Abstract

Given that observational and numerical climate data are being produced at ever more prodigious rates, increasingly sophisticated and automated analysis techniques have become essential. Deep learning is quickly becoming a standard approach for such analyses and, while great progress is being made, major challenges remain. Unlike commercial applications in which deep learning has led to surprising successes, scientific data is highly complex and typically unlabeled. Moreover, interpretability and detecting new mechanisms are key to scientific discovery. To enhance discovery we present a complementary physics-based, data-driven approach that exploits the causal nature of spatiotemporal data sets generated by local dynamics (e.g. hydrodynamic flows). We illustrate how novel patterns and coherent structures can be discovered in cellular automata and outline the path from them to climate data.

4 pages, 1 figure; http://csc.ucdavis.edu/~cmg/compmech/pubs/ci2017_Rupe_et_al.htm

References in corpus (4)

• Computational Mechanics: Pattern and Prediction, Structure and Simplicity
• Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets
• Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems
• ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events

Cited by in corpus (2)

• Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
• The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications