Speaker: Harbir Antil, Department of Mathematical Sciences and Center for Mathematics and Artificial Intelligence, George Mason University

Time: Wednesday, September 23, from 2:30 pm to 3:30 pm

Zoom link for online audience: https://wayne-edu.zoom.us/j/92845590121?pwd=CpRA5Wa5gzSMn2xiVkR2abD83O5nrH.1

Title: Risk-Aware Digital Twins for Biomedical and Engineering Systems: A PDE-Constrained Optimization Perspective

Abstract: Digital twins are adaptive virtual representations of physical systems that are updated by data and used to support prediction, monitoring, and decision-making. Their reliability requires more than simulation or machine learning: it requires physics-based models, scalable optimization, uncertainty quantification, and risk-aware decision support. This talk presents a PDE-constrained optimization perspective on trustworthy digital twins, with emphasis on both applications and theoretical foundations. We discuss how digital twins lead to large-scale inverse, control, and design problems constrained by PDEs, and how adjoint methods, inexact trust-region algorithms, and augmented Lagrangian techniques provide scalable tools for model updating and decision-making. A central theme is uncertainty: digital twins must operate with noisy data, incomplete boundary conditions, uncertain parameters, and rare but consequential events. Risk-aware formulations, including CVaR-type objectives and robust perspectives, provide a principled framework for such settings. Applications include patient-specific aneurysm modeling, structural digital twins for bridges, thermal design, sensing, graph-based control, and Maxwell-based systems.

Bio: Dr. Harbir Antil is a Professor of Mathematical Sciences at George Mason University and Director of the Center for Mathematics and Artificial Intelligence (CMAI). His research focuses on PDE-constrained optimization, shape and topology optimization, reduced order modeling, and deep learning, with applications ranging from fluid dynamics and imaging to gravitational wave detection at Advanced LIGO; his simulations of pathogen propagation in built environments were featured in The New York Times. His work has been funded by the NSF, AFOSR, ONR, and DARPA. He serves as an Associate Editor of SIAM Review, the SIAM Journal on Scientific Computing, and the Journal of Optimization Theory and Applications.

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AIDaS: CAD Seminar Series

Advancing Knowledge, Innovation, and Collaboration in Computation, AI, and Data Science (CAD)

The CAD Seminar Series is a primary seminar series at Wayne State University’s Institute for AI and Data Science (AIDaS). It is a dedicated platform for advancing knowledge, fostering innovation, and promoting collaboration across the fields of Computation, Artificial Intelligence, and Data Science. This series brings together leading experts, researchers, and professionals to explore the latest developments, tackle emerging challenges, and drive forward-thinking solutions at the convergence of these critical disciplines.

Objectives:

• Advance Knowledge: Share cutting-edge research and insights that push the boundaries of what is known in CAD.

• Foster Innovation: Encourage the development of novel ideas and solutions through interdisciplinary dialogue and creative thinking.

• Promote Collaboration: Unite expertise across disciplines and build bridges between academia, industry, and government to address complex problems and create opportunities for joint ventures.

Target Audience: The CAD Seminar Series is designed for a diverse audience, including faculty, researchers, students, and professionals in Computation, AI, Data Science, and related fields. It serves as a forum for exchanging ideas, networking, and contributing to the growth of these rapidly evolving areas. We highly recommend in-person attendance to enhance engagement and networking opportunities with speakers and fellow participants.

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