Qizhi He is an assistant professor in the University of Minnesota’s Department of Civil, Environmental, and Geo- Engineering. His research interests lie primarily at the intersection of computational solid mechanics and data-driven computing, with a focus on the theoretical and numerical development of machine learning-enhanced computational tools for forward and inverse modeling of multiphysics multiscale systems.
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. Before that, he earned his bachelor and master’s degrees from Wuhan University and Dalian University of Technology, respectively.