Jim Crutchfield teaches nonlinear physics at the University of California, Davis, directs its Complexity Sciences Center, and promotes science interventions in nonscientific settings. He's mostly concerned with what patterns are, how they are created, and how intelligent agents discover them; see http://csc.ucdavis.edu/~chaos/
A novel approach to data-driven prediction and unsupervised learning of coherent structures in climate dynamics. Extend our unsupervised machine learning methods in two fundamental ways. The first is that our methods will facilitate pattern discovery—inferring both known patterns and novel, as-yet-unseen patterns and coherent structures from the data. Let the data to tell us the appropriate representations to use to describe patterns, as opposed to selecting a single favorite functional basis or trial-and-error tests to compare them. The second is to adapt our methods to spatiotemporal data—data in which spatial configurations (e.g., velocity vector fields) evolve over time. The goal is to implement structural inference in a principled way that naturally includes temporal dynamics. A wholly new approach, such as this, facilitates the discovery of emergent dynamical patterns in spatiotemporal data is ideally matched to the fundamental algorithmic challenges posed in climate modeling.
- Adam Rupe James P. Crutchfield, 7/31/2018, Local causal states and discrete coherent structures, AIP Chaos: An Interdisciplinary Journal of Nonlinear Science, Academic
- Adam Rupe, 11/11/2017, A Physics-based Approach to Unsupervised Discovery of Spatiotemporal Structures, HPCDevCon17, Academic