  A digital twin is a computer model that represents an individual, for
example, a component, a patient or a process. In many situations, we want to
gain knowledge about an individual from its data while incorporating imperfect
physical knowledge and also learn from data from other individuals. In this
paper, we introduce a fully Bayesian methodology for learning between digital
twins in a setting where the physical parameters of each individual are of
interest. A model discrepancy term is incorporated in the model formulation of
each personalized model to account for the missing physics of the low-fidelity
model. To allow sharing of information between individuals, we introduce a
Bayesian Hierarchical modelling framework where the individual models are
connected through a new level in the hierarchy. Our methodology is demonstrated
in two case studies, a toy example previously used in the literature extended
to more individuals and a cardiovascular model relevant for the treatment of
Hypertension. The case studies show that 1) models not accounting for imperfect
physical models are biased and over-confident, 2) the models accounting for
imperfect physical models are more uncertain but cover the truth, 3) the models
learning between digital twins have less uncertainty than the corresponding
independent individual models, but are not over-confident.
