Tue, Mar 02, 2021 @ 11:00 AM - 12:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Hadi Meidani, Assistant Professor, University of Illinois at Urbana-Champaign
Talk Title: Scientific Machine Learning for Efficient Computational Design of Engineering Systems
The focus of this talk is on using deep neural networks (DNNs) to approximate the response of engineering systems and facilitate their design and control. DNNs can be trained using supervised learning approaches which require large datasets of input-output samples. In engineering applications, these input-output samples are typically obtained from high-fidelity Finite Element or Finite Difference solvers. In applications where these samples are costly to obtain, supervised learning may be prohibitively slow. In this talk, I will present our recent contributions in this domain, which includes (1) using DNNs to accelerate robust topology optimization via a lower-dimensional representation and (2) developing a PDE-based simulation-free deep learning approach that directly exploit the physical laws in an efficient way.
Biography: Hadi Meidani is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. He earned his Ph.D. in Civil Engineering and his M.S. in Electrical Engineering from the University of Southern California in 2012. Prior to joining UIUC, he was a postdoctoral research associate at USC in (2012-2013) and in the Scientific Computing and Imaging Institute at the University of Utah (2013-2014). He is the recipient of the NSF CAREER Award to study fast computational models for infrastructure systems. His research interests are uncertainty quantification, scientific machine learning, and design under uncertainty.
Host: Dr. Roger Ghanem
Location: Zoom: https://usc.zoom.us/j/97228056404; Meeting ID: 972 2805 6404: Passcode: 864779
Audiences: Everyone Is Invited
Contact: Evangeline Reyes