李国平数理科学讲座

Speaker: Prof. Zhen Li (Clemson University, https://cecas.clemson.edu/zhenli/)

Title: Mathematical Foundation of Coarse-Graining and Machine-learning Methods Applied to Multiscale Problems

Time: 2022.05.20, Friday 10:00—11:00 am (Beijing)

Zoom ID: 818 8063 2959  Password: 123456

Abstract:

Multiscale features in many interesting physical systems originate from hierarchical structures across a wide range of temporal and spatial scales beyond the reach of any single simulation method. Mesoscopic methods can seamlessly bridge the gap from microscopic scales to macroscopic scales, and thus become the key scientific approach playing unique roles in research of multiscale problems. In this talk, I will first introduce mathematical and physical foundations of coarse-graining and derive new governing equations of mesoscopic models. I will subsequently present the details of a coarse-graining procedure based on the rigorous theoretical approach of the Mori-Zwanzig (MZ) formalism, by running molecular dynamics simulations and constructing the MZ-informed mesoscopic model directly from atomistic trajectories, as well as computing the memory terms in coarse-grained dynamics. Then, I will showcase how coarse-graining modeling methods are applied to diverse applications. Examples include dissipative particle dynamics modeling of smart materials, biology processes, and multiphase flows. Also, I will introduce two machine-learning approaches applied to multiscale problems, i.e., active-learning applied to multiscale simulations of non-Newtonian fluids and parallel-in-time algorithm applied to physics-informed neural network training.

Biography:

Dr. Zhen Li is Assistant Professor of Mechanical Engineering at Clemson University. He received his B.S. in Engineering Mechanics from Wuhan University in 2005 and Ph.D. in Fluid Mechanics from Shanghai University in 2012. After a short postdoctoral experience in University of California-Merced, he joined the CRUNCH group at Brown University in 2013 as a postdoc and was promoted to research assistant professor in 2016 and then research associate professor in 2019. His research focuses on multiscale modeling for tackling challenges in multiscale/multiphysics problems, in particular on mathematical and physical foundations of coarse-graining, multiscale methods and machine-learning approaches for applications in complex fluids, biology and soft matter physics. Dr. Li has published more than 50 journal papers and 4 book chapters, and released multiple open-source software packages, including the MUI library, the USERMESO2.0 GPU-accelerated particle simulator and the DPD-MESO module of LAMMPS.