Qizhi He's research focuses on the intersection of computational physics and scientific machine learning, advancing both theoretical and numerical developments of next-generation computational tools for modeling multiscale natural and engineered systems. He is an assistant professor in the University of Minnesota’s Department of Civil, Environmental, and Geo- Engineering, leading the Intelligent Computational Mechanics Group.
He’s research spans across multiscale materials modeling, fracture and damage mechanics, PDE-constrained optimization, reduced-order modeling, and deep learning for inverse problems related to porous and composite, and energetic materials. One of his recent interests is identifying and predicting process-induced fracture/defects through data assimilation approach with physics-informed machine learning.
He received his MS in applied mathematics and PhD in structural engineering and computational science from the University of California San Diego in 2018, and he received postdoctoral training at Pacific Northwest National Laboratory. He is a member of the editorial board for the international journal Computers and Geotechnics.