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. In collaboration with the UIUC's team, this study aims at exploring the application of deep learning approach in predicting the flow and transport behavior during CO2 injection, with providing a detailed comparison to the classic FEM numerical solver. More details can be found in: https://authors.elsevier.com/a/1gsEh,63b-0Miq.
Our paper "Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model" by Dongyu Feng (PNNL), Zeli Tan (PNNL) & QiZhi He (UMN) is accepted for publication in Water Resources Research.
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.
Conference (December 12-15, 2022) We have two collaborative studies presented in AGU Fall Meeting 2022, Chicago.
- Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions
- Solving River Dynamics at the River-ocean Interface using a Physics-informed Deep Learning Based Data Assimilation Approach
Event: We are organizing a symposiums titled "Data-Driven Approaches and Digital Twins for Solid and Geological Mechanics" in Engineering Mechanics Institute Conference (EMI) 2023. You are invited to submit abstracts to the mini-symposium MS704 at https://emi-conference.org/call-abstracts/list-mini-symposia
Talk (November, 2022) 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.
In the same week, 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.
Event (November, 2022) 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 abstracts to the following two MS before Jan 15th, 2023.
MS 413 Data-Driven Computational Solid and Geological Mechanics
Qizhi He (UMN), WaiChing Sun (Columbia), Jiun-Shyan Chen (UCSD), Xiaolong He (ANSYS)
MS 605 Physics-Based and Data-Driven Solutions for Additive Manufacturing
Lin Cheng (WPI), Jinhui Yan (UIUC), Miguel Bessa (Brown), Qizhi He (UMN)
News (August, 2022) 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.
Event (August 10, 2022) A farewell dinner at Surly for Yuxiang!
Member (August 2022) Welcome to new CEGE graduate student Honghui Du to He Group!
Paper (July 2022) Our paper “Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery” is published online in Journal of Power Sources. Here, an enhanced version of physics-constrained deep neural network is developed to capture the sharp voltage changes in the extreme SOC regions for flow battery cell. See Link.