报告题目：Model order reduction of optimal mixing via fluid flows
报 告 人：张杨文（卡内基梅隆大学 博士后）
会议时间：2023-1-12 早上9:00开始
会议地点：腾讯会议号364-718-726
报告摘要：The question of what fluid flow maximizes mixing rate, slows it down, or even steers a quantity of interest toward a desired target distribution draws great attention from a broad range of scientists and engineers in the area of complex dynamical systems. Our methodology is to place these problems within a flexible computational framework, and to develop a solution strategy based on optimal control tools, data compression strategies, and methods to reduce the complexity of the mathematical modelsMore precisely, the convection diffusion equation is used to describe the dissipative scalar field advected by the incompressible Stokes flows. Various control designs will be investigated to steer the fluid flows. This essentially leads to a highly challenging nonlinear system. Numerical schemes for the highly complicated optimality system will be constructed and analyzed. To reduce the computational cost, new model order reduction and new incremental data compression techniques are proposed for the problem.
报告人简介：张杨文，2018年在密苏里科技大学获得博士学位，研究方向是偏微分方程的控制的理论和相应的数值分析，博士导师是John Singler. 2018年至2021年在特拉华大学做博士后研究，合作导师是Peter Monk, 期间的研究方向是电磁学. 2021年到至今，在卡内基梅隆大学做博士后，现从事PDE的模型降阶和数据科学相关的工作。
