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Department of Mathematics,
University of California San Diego

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Center for Computational Mathematics Seminar & MINDS Seminar

Li Wang

University of Minnesota

Learning-enhanced structure preserving particle methods for nonlinear PDEs

Abstract:

In the current stage of numerical methods for PDE, the primary challenge lies in addressing the complexities of high dimensionality while maintaining physical fidelity in our solvers. In this presentation, I will introduce deep learning assisted particle methods aimed at addressing some of these challenges.  These methods combine the benefits of traditional structure-preserving techniques with the approximation power of neural networks, aiming to handle high dimensional problems with minimal training. I will begin with a discussion of general Wasserstein-type gradient flows and then extend the concept to the Landau equation in plasma physics.

February 7, 2025

11:00 AM

AP&M 2402 and Zoom ID 946 7260 9849

Research Areas

Mathematics of Information, Data, and Signals

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