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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 (InCOME) Group led by Dr. Qizhi He. Our group specializes in the convergence of computational mechanics, multiscale and multiphysics material modeling, and scientific machine learning, with main focuses on developing innovative mathematical and computational approaches that facilitate a profound understanding of natural and engineered systems. Our current research interests encompass a range of topics, including advanced numerical methods (FEM, meshfree methods, etc.) for materials modeling, data-driven solid mechanics, damage and fracture mechanics, reduced-order modeling, material inverse design and structural optimization, and physics-informed deep learning for geophysical inverse problems. We are interested in advancing the frontiers of integrating scientific machine learning with computational mechanics.

Our overarching goals are:

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

2) Providing high-performance computational methods and software tools for earth/geo-science in support of environmental health, cleaner energy, and national security

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

For more information about our active research, see Research and Publication.


  • (Top) We are always looking for talented, enthusiastic students who are interested in interdisciplinary challenges and computer modeling to join the research group.
  • You are invited to submit an Abstract to "Advances in Multi-Scale Modeling and Machine Learning for Long-Duration Electrochemical Energy Storage Systems" for 2024 ACS Spring Meeting, ENFL.  Due: Oct. 2, 2023. [Link]
  • Invitation to submit abstracts for presentation at the mini-symposium 8-1 "Data-driven Computational Solid Mechanics" at 2023 SES Annual Technical Meeting [Link]

Recent News

  • Paper (04/2023): Congratulations to Honghui for publishing the paper "Neural-Integrated Meshfree (NIM) Method" in Computer Methods in Applied Mechanics and Engineering. This novel framework integrates symbolic basis functions, meshfree discretization, and physics-informed machine learning to efficiently solve PDEs in computational mechanics. Feel free to check the paper in Link. We also look forward to reporting the GPU-enabled NIM for nonlinear elasticity soon!
  • Talk (12/2023): Our group, InCOME, will be contributing 4 presentations at AGU Annual Meeting 2023, 11-15 December. If you are interested in our work, please check out our talks and posters (focusing on hybrid scientific machine learning, geophysics and geomechanics, subsurface transport, carbon sequestration, and ice-sheet modeling). [See details
  • Paper (11/2023): New paper on arXiv: Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics 
  • Talk (10/2023): We are honored to be invited to present our recent work "Neural Meshfree Method: A Hybrid Solver for Computational Mechanics" at Advances in Computational Mechanics (ACM 2023) — a conference celebrating the 80th birthday of Thomas J.R. Hughes, in Austin, Texas, from October 22-25, 2023.
  • Event (10/2023): We hosted a mini-symposium titled "Data-driven Computational Solid Mechanics" during the 2023 SES Annual Technical Meeting. Gratitude to all speakers for their valuable contributions and to our fellow chairs for their generous assistance.
  • Member (09/2023) Welcome to new CEGE graduate students Binyao and Zihan to join the Group!
  • Paper (08/2023): Our paper "A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling" was accepted for publication in Journal of Computational Physics
  • Short course (07/2023): Dr. He joined the instructor team to offer a Short Course "Machine Learning for Solid Mechanics" at 17th U. S. National Congress on Computational Mechanics. The slides of my lab session can be found here.
  • Paper (04/2023): Congratulations to Honghui for publishing a paper titled "Modeling density-driven flow in porous media by physics-informed neural networks for CO2 sequestration" in Computers and Geotechnics. [More]
  • Paper (02/2023): Our paper "Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model" was accepted for publication in Water Resources Research.
  • 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!

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