Exemplary project image


General Page Body

"Scientists study the world as it is; engineers, create the world that has never been." -- Theodore von Karman

Welcome to the Intelligent Computational Mechanics (InCOME) Group led by Dr. Qizhi He. Our group works at the intersection of Computational Engineering and Sciences (CES), Multiscale and Multiphysics Modeling (MMM), and Scientific Machine Learning (SciML), with a focus on areas that involve and advance the application of mechanics, mathematics and numerical methods. Our current research interests include meshfree materials modeling, damage and fracture mechanics, topology optimization, reduced-order modeling, and physics-informed deep learning for inverse problems.

Our overarching goals are:

1) Advancing the fundamental understanding of natural and engineered materials, e.g., porous media, composites, biological tissues and energy storage materials;

2) Providing numerical simulation-based predictive capability to critical problems related to climate, environmental and energy challenges;

3) Improving the sustainability of civil structures and infrastructure under extreme conditions due to climate change and anthropogenic activities.

More information about our active research, see Research and Publication.


We are always looking for talented, enthusiastic students who are interested in interdisciplinary challenges and computer modeling to join the research group.

  • Research Assistant Position for current UMN students: see Hiring Info
  • Invitation to submit abstracts for presentation at USNCCM 17 [More]
  • Invitation to submit abstracts for presentation at the mini-symposium MS704 in Engineering Mechanics Institute Conference (EMI) 2023 [More]

Recent News

  • Paper (01/2023): Our preprint A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling is released.
  • Talk (December, 2022) Dr. He gave a seminar talk about Reduced Order Modeling and Physics-Constrained Deep Surrogate Model at University of Illinois Urbana-Champaign.
  • Talk (November, 2022) PhD student Honghui presented his recent studies on "efficient meshfree method and CO2 density-driven flow modeling by using physics-informed neural networks" in the Structures Seminar at the Department of Civil, Environmental, ang Geo- Engineering.
  • Event: We are organizing two symposiums on Data-Driven Computational Solid Mechanics and Data-Driven Additive Manufacturing in the 17th U.S. National Congress on Computational Mechanics (USNCCM) at Albuquerque, New Mexico, July 23-27, 2023 (https://17.usnccm.org/). Welcome to submit an abstract to MS 413 Data-Driven Computational Solid and Geological Mechanics or MS 605 Physics-Based and Data-Driven Solutions for Additive Manufacturing before Jan 15th, 2023. 
  • Member (August 2022) Welcome to new CEGE graduate student Honghui Du to join He Group!
  • Grant (June 2022) Our group received a U of M Informatics Institute (UMII) seed grant award (Medium) to develop a novel knowledge-augmented machine learning tool for a fast and reliable defect prediction in 3D printing. Congratulations!
  • Member (June 2022) Welcome new group members Ziyang Gao, Amarachi Nzeukwu, and Yuxiang Wan!
  • Talk (May 2022): Dr. He will co-chair the session DS04.04: Accelerating Materials Discovery in Symposium DS04 of the 2022 MRS Spring Meeting on May 8-13, Honolulu. He will also present a recent work on the application of physics-constrained deep neural network for redox flow battery modeling on May 10.
  • Talk (April 2022): Dr. He gave a contributed talk titled "Enhanced Physics-Constrained Deep Neural Networks for the Redox Flow Battery Modeling,” at AIRES 3: Machine Learning for Robust Digital Twins
  • Event: The USACM Thematic Conference on Meshfree and Finite Element Methods with Applications will be held in Berkeley, USA, September 25-27, 2022. Abstracts can be submitted on the conference website [link] and the deadline for abstract submission is May 1, 2022.
  • Paper (Mar 2022) Our paper “Physics-constrained deep neural network method for estimating parameters in a redox flow battery” is accepted for publication in Journal of Power Sources. [More]

[More News]