报告题目；Modeling subgrid effects and temporal splitting in machine learning
报告人:Prof. Yalchin Efendiev （Texas A&M University）
邀请人：李秋齐
报告时间：2022 年 7 月 4 日 (星期一) 3:00 PM
报告形式：在线报告 ( 腾讯会议）
腾讯会议号：428 870 919
入会链接：https://meeting.tencent.com/dm/2vLbt5BmfqJA
报告摘要
In this talk, we will start with some main concepts in multiscale modeling and temporal splitting. Our goal is
to model processes in multiscale media without scale separation and with high contrast. We assume that the
coarse grid doesn’t resolve the scales and the contrast. To deal with these problems, I will introduce multiscale
methods that use multicontinua approaches.
These approaches use additional macroscopic variables.
I
will discuss the convergence of these approaches and show that these methods converge independent of the
contrast. The multicontinua approaches can benefit from machine learning techniques, which I will discuss.
I will also consider how multiscale methods can be used for temporal splitting. High contrast brings stiffness
to the system, which requires small time steps. We will introduce partial explicit methods that construct
time discretizations with the time stepping that is independent of the contrast. Numerical results will be
shown to back up our theories. We will discuss how these approaches are used in machine learning, and will
discuss the general concepts and present some applications.
专家简介
Yalchin Efendiev 教授是美国 Texas A&M University 教授、多尺度问题数值计算领域国际知名专家、美
国数学会会士、美国工业与应用数学会会士、2015 年国际多孔介质协会全球年会大会报告人，2014
年世界数学家大会 45 分钟报告人，SCI 期刊 JCAM 主编。
