Beijing Institute of Mathematical Sciences and Applications Beijing Institute of Mathematical Sciences and Applications

  • About
    • President
    • Governance
    • Partner Institutions
    • Visit
  • People
    • Management
    • Faculty
    • Postdocs
    • Visiting Scholars
    • Staff
  • Research
    • Research Groups
    • Courses
    • Seminars
  • Join Us
    • Faculty
    • Postdocs
    • Students
  • Events
    • Conferences
    • Workshops
    • Forum
  • Life @ BIMSA
    • Accommodation
    • Transportation
    • Facilities
    • Tour
  • News
    • News
    • Announcement
    • Downloads
About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Staff
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
Downloads
Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > Tsinghua-BIMSA Computational & Applied Mathematics (CAM) Seminar Deep BSDE approach for high dimensional PDE problems
Deep BSDE approach for high dimensional PDE problems
Organizer
Computational & Applied Mathematics Group
Speaker
Mo Zhou
Time
Thursday, November 9, 2023 11:30 AM - 1:30 PM
Venue
Online
Online
Tencent 677 1805 8331 ()
Abstract
Solving high-dimensional problems is a challenging task, particularly in the realm of partial differential equations (PDEs). Neural networks have emerged as powerful tools for addressing such complexities. In this presentation, I will provide an overview of deep learning techniques commonly used to tackle PDE problems. Specifically, I will delve into the innovative DeepBSDE method, presenting its theoretical foundations and detailing its application in two scenarios. The first application centers on solving the eigenvalue problem, creatively transformed into a fixed-point formulation reminiscent of a diffusion Monte Carlo approach. In the second application, we harness the actor-critic framework from reinforcement learning to address the Hamilton—Jacobi—Bellman (HJB) equation. To enhance the accuracy of our approach, we introduce a variance-reduced temporal difference method for the critic and implement an adaptive step size algorithm for the actor.
Speaker Intro
Mo Zhou (周默) is an assistant adjunct Professor at UCLA, where he conducts cutting-edge research at the intersection of optimal control, mean-field game problems and deep learning. Currently, he is in Prof. Stan Osher's and Prof. Hayden Schaeffer's research groups. Before joining UCLA, Mo earned his Ph.D. at Duke University, where he was mentored by Prof. Jianfeng Lu. Prior to that, he was an undergraduate at Tsinghua University.
Beijing Institute of Mathematical Sciences and Applications
CONTACT

No. 544, Hefangkou Village Huaibei Town, Huairou District Beijing 101408

北京市怀柔区 河防口村544号
北京雁栖湖应用数学研究院 101408

Tel. 010-60661855
Email. administration@bimsa.cn

Copyright © Beijing Institute of Mathematical Sciences and Applications

京ICP备2022029550号-1

京公网安备11011602001060 京公网安备11011602001060