NTU-CEE Seminar Series: Assistant Professor Qizhi (“KaiChi”) He

Organized By

CEE Seminar Committee

Host By

Assistant Professor Fu Yuguang

Topic

About the Seminar

We present the development of a hybrid computational framework that integrates physicsbased numerical schemes with machine learning methods to address various forward and inverse problems in computational mechanics. Our focus is on applications involving inherent material complexities and coupling effects and exploring how underlying physics laws can be effectively imposed in these methods when different amounts of data are available. We begin by introducing a physics-informed machine learning approach, termed Neural-Integrated Meshfree (NIM), designed to overcome the issues of low accuracy and training efficiency in simulating large-deformations and material nonlinearities.

This approach utilizes a hybrid approximation strategy that combines neural network representations with customized basis functions. The effectiveness of the NIM method is demonstrated through various linear and nonlinear benchmark mechanics problems, including identification of heterogeneous biological materials. Additionally, in scenarios where experimental/simulation data are abundant, we introduce a hybrid scheme that leverages data-driven learning models for solving different coupled systems. We show that the proposed machine learning models can reliably learn operators that capture the underlying physical processes, enabling reduced-order modeling of complex, nonlinear high-dimensional problems. Geophysical and biological examples will be presented to showcase the versatility of these machine learning techniques in enhancing scientific computing.

About the Speaker

Dr. QiZhi ("KaiChi") He is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota (UMN). He obtained his Ph.D. in Structural Engineering and Computational Science from the University of California, San Diego in 2018. From 2019 to 2021, he worked as a postdoctoral research associate in the Advanced Computing, Mathematics, and Data Division at Pacific Northwest National Laboratory.

Dr. He's research focuses on advancing theoretical and numerical methods to predict the mechanics of porous and composite material systems under extreme conditions through machine learning-enabled computational approaches. He is a member of ASCE/EMI technical committees on Computational Mechanics and Machine Learning in Mechanics and serves on the editorial board of Computers and Geotechnics. His current research is funded by Minnesota Department of Transportation, UMN Data Science Initiative, and MnDRIVE Research Computing Seed Grants.

Registration

Click here for registration (Closes on 10 September 2024, 02:50 pm)