We were honored to have Hadi Meidani from University of Illinois present as a Keynote at the 2026 SIMULIA Americas Users Conference in Novi, Michigan, May 13-14, 2026.

Abstract: 

Advances in machine learning are transforming how we model and design engineered systems. This seminar presents recent developments in physics-based neural networks and neural operators for fast, accurate physics-based simulation. We will show how these approaches can lead to a foundation modeling framework with generalization across geometries, boundary conditions and physical properties. Using several 3D industry-scale examples, we show how integrating neural network surrogates and generative models can lead to intelligent digital systems that revolutionize industrial design.
 

Presenter: 

Hadi Meidani

Associate Professor - University of Illinois

Hadi Meidani is an Associate Professor in the Department of Civil and Environmental Engineering, an affiliate member of Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois at Urbana-Champaign (UIUC). His research focuses on physics-informed AI, and digital twins for engineering design. He earned his Ph.D. in Civil Engineering, his M.S. in Electrical Engineering, and his M.S. in Structural Engineering from the University of Southern California (USC). Prior to joining UIUC, he was a postdoctoral scholar in the Department of Aerospace and Mechanical Engineering at USC and in the Scientific Computing and Imaging Institute at the University of Utah. Dr. Meidani is the Chair of the Machine Learning Committee of the ASCE Engineering Mechanics Institute. He is the recipient of an NSF CAREER Award on fast computational models for infrastructure networks. His team have won awards from data competitions on railroad engineering and his research has been sponsored by federal agencies such as National Science Foundation (NSF), DOE, and DOT.