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Computational models of the cardiovascular system are increasingly used forthe diagnosis, treatment, and prevention of cardiovascular disease. Beforebeing used for translational applications, the predictive abilities of thesemodels need to be thoroughly demonstrated through verification, validation, anduncertainty quantification. When results depend on multiple uncertain inputs,sensitivity analysis is typically the first step required to separate relevantfrom unimportant inputs, and is key to determine an initial reduction on theproblem dimensionality that will significantly affect the cost of alldownstream analysis tasks. For computationally expensive models with numerousuncertain inputs, sample-based sensitivity analysis may become impractical dueto the substantial number of model evaluations it typically necessitates. Toovercome this limitation, we consider recently proposed Multifidelity MonteCarlo estimators for Sobol' sensitivity indices, and demonstrate theirapplicability to an idealized model of the common carotid artery. Variancereduction is achieved combining a small number of three-dimensionalfluid-structure interaction simulations with affordable one- andzero-dimensional reduced order models. These multifidelity Monte Carloestimators are compared with traditional Monte Carlo and polynomial chaosexpansion estimates. Specifically, we show consistent sensitivity ranks forboth bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variancereduction compared to traditional single-fidelity Monte Carlo estimators forthe same computational budget. As the computational burden of Monte Carloestimators for Sobol' indices is significantly affected by the problemdimensionality, polynomial chaos expansion is found to have lower computationalcost for idealized models with smooth stochastic response.

Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for carotid artery haemodynamics