Title: Physics-Guided AI for Learning Spatiotemporal Dynamics
Applications such as public health, transportation, and climate science often require learning complex dynamics from large-scale spatiotemporal data. While deep learning has shown tremendous success in these domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this talk, I will demonstrate how to principally integrate physics in AI models and algorithms to achieve both prediction accuracy and physical consistency. I will showcase the application of these methods to problems such as forecasting COVID-19, traffic modeling, and accelerating turbulence simulations.
Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She was a Postdoctoral Fellow at the California Institute of Technology. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. She has won Faculty Research Awards from Google, Amazon, and Adobe, several Best Paper Awards, Best Dissertation Award from USC, and was nominated as one of the ‘MIT Rising Stars in EECS’.