Mathematical and Computational Foundations for Enabling Predictive Digital Twins at Scale
Abstract: A digital twin is a set of coupled computational models that evolves over time to persistently represent the structure, behavior and context of a unique physical asset such as a component, system or process. There is a growing interest in the potential of digital twins to revolutionize decision-making across scientific, engineering, medical and societal applications. 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 talk presents 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 and enabled by physics-based reduced-order models. We demonstrate how the approach is instantiated to enable a structural digital twin of an unmanned aerial vehicle.