清华主页 EN
导航菜单

Deep adaptive sampling for numerical PDEs

来源: 11-24

时间:2022-11-24 10:00

地点:Venue: BIMSA 1129B Zoom: 537 192 5549 PW: BIMSA

主讲人:TaoZhou(周涛) Academy of Mathematics and Systems Science, CAS


返回顶部
相关文章
  • Deep Generative Models

    Description: This course introduces how to develop deep generative models (DGMs) by integrating probabilistic graphical models and deep learning to generate realistic data including images, texts, graphs, etc. Course contents include 1) basics of probabilistic graphical models, including Bayesian network and Markov random field; 2) posterior inference methods, including message passing, variat...

  • Deep learning of multi-scale PDEs based on data generated from particle methods

    AbstractSolving multiscale PDEs is difficult in high dimensional and/or convection dominant cases. The Lagrangian computation, interacting particle method, is shown to outperform solving PDEs directly (Eulerian). Examples include computing effective diffusivities, KPP front speed, and asymptotic transport properties in topological insulators. However the particle simulation takes long before co...