Distinguished Seminar in Computational Science and Engineering

Distinguished Seminar in Computational Science and Engineering

May 8, 2025, 12-1PM

45-432 in Building 45 and Zoom Webinar

Data Driven Modeling for Scientific Discovery and Digital Twins

Dongbin Xiu
Professor and Ohio Eminent Scholar
Department of Mathematics
The Ohio State University

Abstract: 

We present a data-driven modeling framework for scientific discovery, termed Flow Map
Learning (FML). This framework enables the construction of accurate predictive models
for complex systems that are not amenable to traditional modeling approaches. By
leveraging measurement data and the expressiveness of deep neural networks (DNNs),
FML facilitates long-term system modeling and prediction even when governing equations
are unavailable. FML is particularly powerful in the context of Digital Twins, an emerging concept in digital transformation. With sufficient offline learning, FML enables the construction of simulation models for key quantities of interest (QoIs) in complex Digital Twins, even when direct mathematical modeling of the QoI is infeasible. During the online execution of a Digital Twin, the learned FML model can simulate and control the QoI without reverting to the computationally intensive Digital Twin itself. As a result, FML serves as an enabling methodology for real-time control and optimization of the physical twin, significantly enhancing the efficiency and practicality of Digital Twin applications.

Bio:

Dongbin Xiu received his Ph.D. in Applied Mathematics from Brown University in 2004.
He joined the Department of Mathematics at Purdue University in 2005 and moved to the
University of Utah in 2013. In 2016, he joined The Ohio State University as a Professor
of Mathematics and an Ohio Eminent Scholar.

Dr. Xiu received the NSF CAREER Award in 2007 and was elected a SIAM Fellow in
2023. He has served on editorial boards of many journals and is currently the Editor-inChief of the Journal of Computational Physics. Additionally, he is the founding Associate Editor-in-Chief of the International Journal for Uncertainty Quantification (IJUQ) and the founding Editor-in-Chief of the Journal of Machine Learning for Modeling and Computing (JMLMC).

His current research focuses on developing efficient numerical methods for data-driven
modeling, scientific machine learning, Digital Twins, and uncertainty quantification.

Data Driven Modeling for Scientific Discovery and Digital Twins
Dongbin Xiu
The Ohio State University