比利时vs摩洛哥足彩
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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****************************
