Adventures in Hybrid Physics-Machine Learning for Multi-scale Climate Modeling, AI-assisted Climate Model Inter-comparison, and ... NVIDIA
Speaker: Mike Pritchard
Date: November 3, 2022
Time: 3:00 p.m.
Format: Hybrid
Virtual: Zoom link provided upon registration
Register here: https://www.eventbrite.com/e/450916823357
In-person: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Room 202, New York, NY 10027
Abstract Excerpt: Low cloud forming turbulence is a key source of climate model prediction uncertainty that, despite seeming unapproachable to simulate on planetary scales, could soon come into computational range with hybrid machine learning methods. I will discuss a chain of recent work driving in this direction that has tried to outsource explicit computations within “multi-scale” climate models to simple neural networks. Focus will be on the unsolved challenge of controlling stubborn prognostic error growth in such hybrid AI climate models and especially the emerging potential of physical renormalizations to achieve “climate invariance.”
Bio: Mike Pritchard serves on the LEAP Executive and Convergence Committees as the Director for Institutional Integration. Mike is an Associate Professor in the Department of Earth System Sciences at the University of California, Irvine, where he studies the planetary water cycle using next-gen global atmospheric simulations and machine learning. Beginning in July 2022, he took on an additional role as a Director for Climate Simulation Research at NVIDIA
