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4066金沙官网:20220704 Yalchin Efendiev Modeling subgrid effects and temporal splitting in machine learning

发布时间:2022-07-01 11:00    浏览次数:    来源:

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。


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