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We present a complementary physics based, unsupervised 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.

Event Name

IXPUG Annual Fall Conference 2018


unsupervised learning,parallel programming