- Applied and Computational Mathematics Seminar
- Monday, September 13, 2021 - 14:00 for 1 hour (actually 50 minutes)
- Prof. Rose Yu – UCSD
While deep learning has been used for dynamics learning, limited physical accuracy and an inability to generalize under distributional shift limit its applicability to real world. In this talk, I will demonstrate how to incorporate symmetries into deep neural networks and significantly improve the physical consistency, sample efficiency, and generalization in learning dynamics. I will showcase the applications of these models to challenging problems such as turbulence forecasting and trajectory prediction for autonomous vehicles.