Professor Karen E. Willcox, Director, Oden Institute for Computational Engineering and Sciences, University of Texas, Austin
Predictive digital twins: from physics-based modeling to scientific machine learning

Abstract
A digital twin is a scalable virtual model that reflects an individual physical asset throughout its lifecycle. The key to the digital twin concept is the ability to sense, collect, analyze and learn from asset data. To make digital twins a reality, many elements from the interdisciplinary field of computer science, including physics-based modeling and simulation, inverse problems, uncertainty quantification, and scientific machine learning, have a role important to play.

In this work, we develop a probabilistic graphical model as a formal mathematical representation of a digital twin and its associated physical asset. We create an abstraction of the twin asset system as a set of coupled dynamic systems, evolving over time through their respective state spaces and interacting via observed data and control inputs. The abstraction is carried out computationally in the form of a dynamic decision network. Predictive capabilities are enabled by physics-based reduced-order models. We demonstrate how the approach is instantiated to create, update, and deploy a structural digital twin of an unmanned aerial vehicle.

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