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Found 5 papers, 3 papers with code
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization
Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium.
Spacetime Autoencoders Using Local Causal States
Local causal states are latent representations that capture organized pattern and structure in complex spatiotemporal systems.
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems
Extracting actionable insight from complex unlabeled scientific data is an open challenge and key to unlocking data-driven discovery in science.
Towards Unsupervised Segmentation of Extreme Weather Events
Extreme weather is one of the main mechanisms through which climate change will directly impact human society.
Local Causal States and Discrete Coherent Structures
The approach is behavior-driven in the sense that it does not rely on directly analyzing spatiotemporal equations of motion, rather it considers only the spatiotemporal fields a system generates.
