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

Welcome to the Intelligent Computational Mechanics Group led by Dr. Qizhi He. Our group works at the intersection of computational mechanics, multiscale materials modeling, and scientific machine learning, with a focus on developing novel computational tools by combining high-fidelity scientific computing and artificial intelligence to enable effective modeling, simulation and design capabilities of complex physical systems involving multiple phases, scale lengths, and physical processes. 

Our mission is to advance our fundamental understanding of natural and engineered materials (porous media, composites, energy storage materials, biological tissues, etc.), provide simulation-based digital solution framework to critical problems related to environmental and energy challenges, and promote the sustainability and resilience of structure and infrastructure under extreme conditions.


The main directions of our group are in scientific machine learning, topology optimization, and meshfree based computational modeling and simulation. Current UMN students interested in any of above areas are encouraged to contact Dr. He.

Funded Research Assistant Position for current UMN students: see Hiring Info

Recent News

  • 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]
  • Talk (Mar, 2022) Dr. He gave a seminar titled "Machine Learning Enhanced Computational Mechanics for Materials Modeling" in the CEAS Seminar at the University of Wyoming on Mar 10, 2022.

[More News]