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This work proposes an approach that combines a library of component-based reduced-order models with Bayesian state estimation in order to create data-driven physics-based digital twins. Reduced-order modeling produces physics-based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component-based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation-both critical features in the digital twin context. Data-driven model adaptation and uncertainty quantification is formulated as a Bayesian state estimation problem, in which sensor data is used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics-based digital twin of the aircraft structure. The data-driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation.

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