A probabilistic graphical model foundation for enabling predictive digital twins at scale

By Michael G. Kapteyn, Jacob V. R. Pretorius, Karen E. Willcox. Nature Computational Science, 2020

331 citations recorded by the source corpus.

A unifying mathematical formulation is needed to move from one-off digital twins built through custom implementations to robust digital twin implementations at scale. This work proposes 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 formal definition of this coupled system as a probabilistic graphical model enables us to draw upon well-established theory and methods from Bayesian statistics, dynamical systems and control theory. The declarative and general nature of the proposed digital twin model make it rigorous yet flexible, enabling its application at scale in a diverse range of application areas. We demonstrate how the model is instantiated to enable a structural digital twin of an unmanned aerial vehicle (UAV). The digital twin is calibrated using experimental data from a physical UAV asset. Its use in dynamic decision-making is then illustrated in a synthetic example where the UAV undergoes an in-flight damage event and the digital twin is dynamically updated using sensor data. The graphical model foundation ensures that the digital twin calibration and updating process is principled, unified and able to scale to an entire fleet of digital twins. This work proposes a probabilistic graphical model as a formal mathematical foundation for digital twins, and demonstrates how this model supports principled data assimilation, optimal control and end-to-end uncertainty quantification.

Subfields: Artificial Intelligence, Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/931306769105f76da3f6987ad342370ede00be10

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Citations in the corpus

A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “A recent paper demonstrated a structural digital twin of a UAV and provided a distinction between a physical state and a digital state (Kapteyn et al. 2021).”
Fusing Blockchain and AI With Metaverse: A Survey (2022, background): “Not only metaverse users, but the objects or things in the physical world also interact with the metaverse, evolving to persistently represent the structure, behaviors, and context of a unique physical asset (such as a component, a human, or a process) [45] in the virtual world.”