2024

• 12/2024 Paper: Our paper, which focuses on advancing the differentiable meshfree method with GPU acceleration for modeling nonlinear elasticity problems and characterization of heterogeneous mechanical properties, has been published in Engineering with Computers [Link]. Tutorial code is available on GitHub. 
   
• 12/2024 Award: Binyao was awarded the ADC Graduate Fellowship by the Data Science Initiative, which will support his research on AI-Enabled Multiphysics Design and Simulations.
   
• 11/2024 Conference: Recent updates from IMECE 2024 in Portland, OR:
  Our group was involved in two talks:
  1. "A Neural Network-Enhanced Differentiable Meshfree Method for Computational Mechanics"
  2. "Neural Topology Optimization Based on Differential Programming with Principled Constrained Optimization"
  Also, congratulations to Honghui for receiving the ASME Applied Mechanics Division's Robert M. and Mary Haythornthwaite Foundation Student Travel Award for the work on "Neural-Integrated Meshfree Method for computational mechanics". [See more]
   

• 09/2024 Talk: Thank Prof. Wada for inviting me to present at the workshop "Deep and Machine Learning Methodology in the Context of Application to Computational Engineering" during IWACOM-IV in Kitakyushu, Japan. I’m excited to share my work and engage with colleagues on advancements in this area.

• 09/2024 Talk: Dr. He delivered a seminar on differentiable solid mechanics and geophysics, titled "Neural-Integrated and Data-Driven Approaches for Computational Modeling in Solid Mechanics and Geophysics," at NUS, NTU and A*STAR in Singapore. [News]

• 07/2024 Announcement : This year, IMECE 2024 is introducing a new "Best Paper Competition" to recognize the top 10 AI/Deep Learning-related papers presented at the conference. I am thrilled to have been invited to serve on the ASME AI/Deep Learning Best Paper Honors Committee. I encourage you to nominate outstanding AI papers from your fields, and I’m excited to see the innovative work that will be submitted! [See Link]

• 07/2024 Paper: Our new paper, "Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain Hyperelasticity," is now available on arXiv. In this paper, we discuss how to accurately simulate nonlinear elastic materials, such as rubbers, biotissues, and beam structures under large deformation, by a novel machine learning method, while bypassing the use of consistent tangent stiffness and Newton's method conventionally required in FEM solvers. Additionally, the same framework can handle complex material identification for biological tissues. Check it out here!

• 07/2024 Short course: In the upcoming WCCM 2024 / PANACM 2024 conference, we are co-organizing a short course titled "Machine Learning for Solid Mechanics". Qizhi He will deliver a lecture and a lab on Manifold Learning and Data-Driven Computing for Nonlinear Solid Mechanics. 

• 04/2024 Paper: 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!

  2023

• 12/2023 Conference: 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] 

• 11/2023 Paper: New paper on arXiv: Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics 

• 10/2023 Conference: 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.

• 10/2023 Conference: 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.

• 09/2023 News: Welcome to new CEGE graduate students Binyao and Zihan to join the Group!

• 08/2023 Paper: Our paper "A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling" was accepted for publication in Journal of Computational Physics. 

• 07/2023 Short course: 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.

• 04/2023 Paper: 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]

• 02/2023 Paper: 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.

• 01/2023 Paper: Our preprint A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling is released.

• 01/2023 Conference: 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. 

  2022

• 12/2022 Talk: Dr. He gave a seminar talk about Reduced Order Modeling and Physics-Constrained Deep Surrogate Model at University of Illinois Urbana-Champaign.

• 11/2022 Talk: 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.

• 11/2022 Talk: Dr. He gave a talk titled "Data-Assisted Computational Mechanics: From Reduced Order Modeling to Physics-Constrained Deep Surrogate Model" in Solid Mechanics Research Seminar at the Department of Aerospace Engineering and Mechanics.

• 08/2022 News: Dr. He was appointed as the CTS Faulty Scholar of the Center for Transportation Studies. He also recently joined the organizer team for the "CSE DSI Machine Learning Seminar Series sponsored by the College of Science and Engineering.

• 08/2022 Paper: A collaborative work with UC San Diego's teams is accepted for publication in the ASME-Journal of Biomechanical Engineering. Congratulations! 

• 08/2022 News: Welcome to new CEGE graduate student Honghui Du to join He Group!

• 06/2022 Award: Our lab received a 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!