Qizhi He

Qizhi He in his office

Qizhi (“KaiChi”) He

Assistant Professor, Department of Civil, Environmental, and Geo- Engineering

Contact

Civil Engineering Building
Room 240
500 Pillsbury Drive SE
Minneapolis, MN 55455

Education

  • Ph.D., 2018, Structural Engineering with Specialization in 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, China
  • B.S., 2010, Engineering Mechanics, Wuhan University, China

Professional Background

  • Assistant Professor, Department of Mechanical Engineering, San Diego State University, 2021-2021
  • Postdoctoral Research Associate, Pacific Northwest National Laboratory (PNNL), 2019-2021
Research Interests

He Research Group

Qizhi He works at the intersection of computational mechanics, materials modeling, and scientific machine learning. His research focuses on developing computational tools that synergize mechanistic, data-driven, and artificial intelligence approaches to modeling the mechanics of complex multiphysical processes. The goal of his 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.

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.