UMN Machine Learning Seminar: Machine Learning Enhanced Computational Mechanics for Materials Modeling

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Wednesday from 12 p.m. - 1 p.m. during the Spring 2022 semester.

This week's speaker, Qizhi He (Department of Civil, Environmental, and Geo- Engineering of UMN), will be giving a talk titled "Machine Learning Enhanced Computational Mechanics for Materials Modeling".

Abstract

It remains challenging in constitutive modeling and large-scale simulation due to the inherent complexities of materials such as inelasticity and heterogeneities. This talk will survey our recent research on developing hybrid computational methods by combining physics-based models, data-driven machine learning techniques, and mesh-free schemes to address various difficulties in computational mechanics arising from material characterization, modeling, and reduction.

First, I will introduce a physics-constrained data-driven modeling framework, which enables predictive physical simulation directly from material data without the employment of phenomenological constitutive models. A new approach inspired by manifold learning is formulated under the Galerkin meshfree methods for modeling nonlinear solids and biological tissues. Second, I will present how to use manifold learning together with sparse sampling to construct effective low-dimensional models of nonlinear mechanics systems. This hyper-reduction method has been applied to fast prediction of thermal fatigue behaviors of electronic packages. Lastly, I will discuss our recent work on developing physics-informed deep learning framework for discovering and identifying hidden constitutive models with a specific application to subsurface flow and transport in heterogeneous porous media. The goal of this study is to develop a computational framework that allows a seamless fusion of measurement data and the information from widely accepted physical laws.

Biography

Qizhi He is an Assistant Professor in the Department of Civil, Environmental, and Geo- Engineering at the University of Minnesota. He received his M.A. in applied mathematics and Ph.D. in structural engineering and computational science from UC San Diego, in 2016 and 2018, respectively. Afterwards, he was a Postdoctoral Research Associate at Pacific Northwest National Laboratory (PNNL), where he developed scientific machine learning methods for modeling flow and transport processes in porous media. His current research interests lie at the intersection of computational mechanics, materials modeling, and data-driven computing, with a focus on advancing data-driven machine learning enabled computational tools to predict mechanics of complex multiphysical processes and improve our fundamental understanding of multiscale materials and structures in engineered and natural systems.