"Predictive Digital Twins: From physics-based modeling to scientific machine learning" Prof. Willcox
CIS Digital Twin Days 2021 | 15 Nov. 2021 | Lausanne Switzerland
Prof. 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 an evolving virtual model that mirrors an individual physical asset throughout its lifecycle. Key to the digital twin concept is the ability to sense, collect, analyze, and learn from the asset’s data. To make digital twins a reality, many elements of the interdisciplinary field of computational science, including physics-based modeling and simulation, inverse problems, uncertainty quantification, and scientific machine learning, have an important role 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 asset-twin system as a set of coupled dynamical systems, evolving over time through their respective state-spaces and interacting via observed data and control inputs. The abstraction is realized computationally as 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.
cis.epfl.ch
CIS Digital Twin Days 2021 | 15 Nov. 2021 | Lausanne Switzerland
Prof. 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 an evolving virtual model that mirrors an individual physical asset throughout its lifecycle. Key to the digital twin concept is the ability to sense, collect, analyze, and learn from the asset’s data. To make digital twins a reality, many elements of the interdisciplinary field of computational science, including physics-based modeling and simulation, inverse problems, uncertainty quantification, and scientific machine learning, have an important role 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 asset-twin system as a set of coupled dynamical systems, evolving over time through their respective state-spaces and interacting via observed data and control inputs. The abstraction is realized computationally as 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.
cis.epfl.ch
