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A digital twin is a computer model that represents an individual, forexample, a component, a patient or a process. In many situations, we want togain knowledge about an individual from its data while incorporating imperfectphysical knowledge and also learn from data from other individuals. In thispaper, we introduce a fully Bayesian methodology for learning between digitaltwins in a setting where the physical parameters of each individual are ofinterest. A model discrepancy term is incorporated in the model formulation ofeach personalized model to account for the missing physics of the low-fidelitymodel. To allow sharing of information between individuals, we introduce aBayesian Hierarchical modelling framework where the individual models areconnected through a new level in the hierarchy. Our methodology is demonstratedin two case studies, a toy example previously used in the literature extendedto more individuals and a cardiovascular model relevant for the treatment ofHypertension. The case studies show that 1) models not accounting for imperfectphysical models are biased and over-confident, 2) the models accounting forimperfect physical models are more uncertain but cover the truth, 3) the modelslearning between digital twins have less uncertainty than the correspondingindependent individual models, but are not over-confident.

Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes