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A central problem in biomechanical studies of personalised human leftventricular (LV) modelling is estimating the material properties andbiophysical parameters from in-vivo clinical measurements in a time framesuitable for use within a clinic. Understanding these properties can provideinsight into heart function or dysfunction and help inform personalisedmedicine. However, finding a solution to the differential equations whichmathematically describe the kinematics and dynamics of the myocardium throughnumerical integration can be computationally expensive. To circumvent thisissue, we use the concept of emulation to infer the myocardial properties of ahealthy volunteer in a viable clinical time frame using in-vivo magneticresonance image (MRI) data. Emulation methods avoid computationally expensivesimulations from the LV model by replacing the biomechanical model, which isdefined in terms of explicit partial differential equations, with a surrogatemodel inferred from simulations generated before the arrival of a patient,vastly improving computational efficiency at the clinic. We compare andcontrast two emulation strategies: (i) emulation of the computational modeloutputs and (ii) emulation of the loss between the observed patient data andthe computational model outputs. These strategies are tested with two differentinterpolation methods, as well as two different loss functions...

Fast Parameter Inference in a Biomechanical Model of the Left Ventricle using Statistical Emulation