Scholar

Qizhi He

Google Scholar ID: lZ4PEUAAAAAJ

Computing, Intelligence & Mechanics, University of Minnesota - Twin Cities

Computational mechanicsDifferentiable meshfreeScientific MLMaterial failureMultiphysics

Citations & Impact

All-time

Citations

1,315

H-index

i10-index

Publications

Co-authors

Resume

Academic Achievements

Published paper 'Robust data-driven constitutive modeling' (Guo et al.) online in CMAME; another collaborative paper 'Integrating Physics-Informed and Data-Driven Neural Networks into Earth System Models: A Comparative Study for Compound Flood Simulation at River-Ocean Interfaces' accepted by Journal of Geophysical Research: Machine Learning and Computation.

Research Experience

1) Investigate multiphysics phenomena and damage mechanisms in heterogeneous porous media and composite materials.
2) Develop next-generation computational methods and algorithms.
3) Advance the knowledge of scientific machine learning and reduced-order modeling.

Background

Focused on leveraging Computational Mechanics, Scientific Computing, and Artificial Intelligence to address resilience and sustainability challenges related to materials, civil structures, and geosystems under extreme conditions.

Miscellany

Looking for talented, enthusiastic students (both undergraduate and graduate) interested in interdisciplinary challenges and computer modeling to join the research group.