Speaker: Li Wang, University of Minnesota
Title: 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.
Bio: Li Wang is an Associate Professor at the School of Mathematics, University of Minnesota. Her general research interests lie in applied mathematics, numerical analysis and scientific computing.