  Uncertainty quantification (UQ) is a vital step in using mathematical models
and simulations to take decisions. The field of cardiac simulation has begun to
explore and adopt UQ methods to characterise uncertainty in model inputs and
how that propagates through to outputs or predictions. In this perspective
piece we draw attention to an important and under-addressed source of
uncertainty in our predictions -- that of uncertainty in the model structure or
the equations themselves. The difference between imperfect models and reality
is termed model discrepancy, and we are often uncertain as to the size and
consequences of this discrepancy. Here we provide two examples of the
consequences of discrepancy when calibrating models at the ion channel and
action potential scales. Furthermore, we attempt to account for this
discrepancy when calibrating and validating an ion channel model using
different methods, based on modelling the discrepancy using Gaussian processes
(GPs) and autoregressive-moving-average (ARMA) models, then highlight the
advantages and shortcomings of each approach. Finally, suggestions and lines of
enquiry for future work are provided.
