Pattern Discovery:
Physics-Driven Causal Inference
Spatiotemporal Computational Mechanics—
Developing a Novel Approach to Automatically Modeling Coherent Structures and Predicting Turbulent Flows in Climate Dynamics
An Intel Parallel Computing Center
195 Physics
University of California at Davis
530-752-0600
We are developing a novel approach to data-driven prediction and unsupervised learning of coherent structures in climate dynamics. The project extends 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 data. We let the data tell us the appropriate representations that describe patterns, as opposed to selecting a single favorite functional basis or trial-and-error tests to compare them. The second adapts 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, that facilitates the discovery of emergent dynamical patterns in spatiotemporal data is ideally matched to the fundamental algorithmic challenges posed in climate modeling.
Welcome
NEWS!
$12.5M in grants for complexity, networks research (UCD article): CSC wins two Multidisciplinary University Research Initiatives, greatly expanding its complex systems research. Also, see Funding
