Title:

Learning What Matters: Scalable Offline and Online Calibration of Digital Twins

Abstract:

Digital twins promise continuous, high-fidelity representations of physical systems, yet their practical deployment is often constrained by the computational cost of simulation and calibration. This talk presents a unified perspective on making digital twin calibration more efficient, adaptive, and scalable by learning where computational effort matters most.

The first part addresses offline calibration of block-structured models. I introduce a doubly importance-driven calibration method that learns which parameter blocks and which observations are most informative, and directs simulation effort toward the parts of the problem that contribute most to the optimization. This targeted allocation substantially reduces computational cost while preserving calibration accuracy.

The second part turns to online calibration, where model parameters evolve over time and computational resources are limited. The proposed framework combines fast, edge-side surrogate filtering with periodic, cloud-side discrepancy correction based on high-fidelity simulations. This two-tier architecture enables real-time parameter adaptation while maintaining consistency with the underlying digital twin.

Together, these methods show how learning what matters—in parameters, observations, and computation—can support digital twins that remain accurate, scalable, and continuously adaptive in practice.

Bio:

Dr. Eunshin Byon is a Professor in the Department of Industrial and Operations Engineering at the University of Michigan, Ann Arbor, where she also serves as Director of the Master's Program. She received her Ph.D. in Industrial and Systems Engineering from Texas A&M University in 2010. Her research spans data science, digital twin modeling and analysis, and quality and reliability engineering, with applications in energy, healthcare, and manufacturing systems. She served as Chair of the Quality, Statistics, and Reliability (QSR) Section of INFORMS in 2019–2020, and her research group has received multiple research and teaching awards from INFORMS, IISE, and IEEE. Dr. Byon is currently a Senior Editor for the INFORMS Journal on Data Science (2024–present) and a Department Editor for IISE Transactions (2021–present). She previously served as an Associate Editor for IISE Transactions (2019–2021), the INFORMS Journal on Data Science (2020–2024), and IEEE Transactions on Automation Science and Engineering (2019–2021).