Title: Physics-Guided Deep Learning for Climate Science
Abstract: While deep learning has shown tremendous success in many scientific domains, it remains a grand challenge to incorporate first principles in a systematic manner into such models. In this talk, I will demonstrate how to incorporate physical principles such as symmetry, conservation, and multi-scale into deep neural networks for forecasting and uncertainty quantification. I will showcase the applications of these models to challenging problems in climate science. Our methods demonstrate significant improvement in physical consistency, sample efficiency, and generalization in complex spatiotemporal dynamics.
Bio: Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at USC in 2017. She was subsequently a Postdoctoral Fellow at Caltech. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. Among her awards, she has won NSF CAREER Award, Faculty Research Award from JP Morgan, Facebook, Google, Amazon, and Adobe, Several Best Paper Awards, Best Dissertation Award at USC, and was nominated as one of the ’MIT Rising Stars in EECS’.