  Personalized virtual heart models have demonstrated increasing potential for
clinical use, although the estimation of their parameters given
patient-specific data remain a challenge. Traditional physics-based modeling
approaches are computationally costly and often neglect the inherent structural
errors in these models due to model simplifications and assumptions. Modern
deep learning approaches, on the other hand, rely heavily on data supervision
and lacks interpretability. In this paper, we present a novel hybrid modeling
framework to describe a personalized cardiac digital twin as a combination of a
physics-based known expression augmented by neural network modeling of its
unknown gap to reality. We then present a novel meta-learning framework to
enable the separate identification of both the physics-based and neural
components in the hybrid model. We demonstrate the feasibility and generality
of this hybrid modeling framework with two examples of instantiations and their
proof-of-concept in synthetic experiments.
