Qizhi He

Qizhi (“KaiChi”) He
Assistant Professor, Department of Civil, Environmental, and Geo- EngineeringContact
Civil Engineering Building Room 240 500 Pillsbury Drive SEMinneapolis, MN 55455
Education
- Ph.D., 2018, Structural Engineering & Computational Science, University of California San Diego
- M.A., 2016, Applied Mathematics, University of California San Diego
- M.S., 2013, Computational Mechanics, Dalian University of Technology
- B.S., 2010, Engineering Mechanics, Wuhan University
Professional Background
- Assistant Professor, University of Minnesota, 01/2022 – current
- Collaborative Researcher, Scientific Machine Learning Group, Pacific Northwest National Laboratory, 08/2021-current
- Assistant Professor of Mechanical Engineering, San Diego State University, Fall 2021
- Postdoctoral Research Associate, Computational Mathematics Group, Pacific Northwest National Laboratory, 2019-2021
Our group works at the intersection of computational mechanics, materials modeling, and scientific machine learning, with a focus on areas that involve and advance the application of mechanics, mathematics, and numerical methods. The goal of our research is to improve fundamental understanding of multiscale materials and structures and advance the predictive simulation and design capabilities for complex engineered and natural systems. Our research interests include:
- Finite element and meshfree methods for nonlinear mechanics
- Machine learning enhanced computational mechanics
- Data-driven mechanics
- Fracture mechanics
- Multiscale materials modeling
- Reduced-order modeling
- Subsurface flow and transport
- Multi-physics modeling of geological, composite and energetic materials
- Topology optimization
- Musculoskeletal biomechanics
- Scientific machine learning for geo-mechanics & geo-sciences
- Physics-informed deep learning for inverse problems
Selected Publications
He, X., He, Q., Chen, J. S. (2021) Deep autoencoders for physics-constrained data-driven nonlinear materials modeling. Computer Methods in Applied Mechanics and Engineering.
He, Q., Tartakovsky, A. (2021) Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations. Water Resources Research.
Kaneko, S., Wei, H., He, Q., Chen, J. S., Yoshimura, S. (2021) A hyper-reduced meshfree method for fast prediction of thermal fatigue behaviors of electronic packages. Journal of the Mechanics and Physics of Solids.
He, Q., Laurence, D., Lee, C. H., Chen, J. S. (2020). Manifold learning-based data-driven modeling for soft biological tissues. Journal of Biomechanics, 110124.
He, Q., Barajas-Solano, D., Tartakovsky, G., Tartakovsky, A. (2020) Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport. Advances in Water Resources, 141, 103610.
He, Q. & Chen, J. S. (2019) A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database. Computer Methods in Applied Mechanics and Engineering, 363, 112791.
He, Q., Chen, J. S., & Marodon, C. (2019). A decomposed subspace reduction for fracture mechanics based on the meshfree integrated singular basis function method. Computational Mechanics, 63(3), 593- 614.
He, Q., Wei, H., Chen, J. S., Wang, H. P., & Carlson, B. E. (2018). Analysis of hot cracking during lap joint laser welding processes using the melting state-based thermomechanical modeling approach. The International Journal of Advanced Manufacturing Technology, 94(9-12), 4373-4386.
He, Q., Kang, Z., & Wang, Y. (2014). A topology optimization method for geometrically nonlinear structures with meshless analysis and independent density field interpolation. Computational Mechanics, 54(3), 629-644.