Rose Yu - Incorporating Symmetry for Learning Spatiotemporal Dynamics

Prof. Rose Yu from UCSD speaking in the UW Data-driven methods in science and engineering seminar on Nov. 5, 2021.

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Abstract: While deep learning has shown tremendous success in many scientific domains, it remains a grand challenge to incorporate physical principles into such models. In this talk, I will demonstrate how to incorporate symmetries into deep neural networks and significantly improve physical consistency, sample efficiency, and generalization in learning spatiotemporal dynamics. I will showcase the applications of these models to challenging problems such as turbulence forecasting and trajectory prediction for autonomous vehicles.