A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale

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 as a Bayesian network to create a structural digital twin of an unmanned aerial vehicle. The graphical model foundation ensures that the digital twin creation and updating process is principled, repeatable, and able to scale to the calibration of an entire fleet of digital twins.