  Boundary condition (BC) calibration to assimilate clinical measurements is an
essential step in any subject-specific simulation of cardiovascular fluid
dynamics. Bayesian calibration approaches have successfully quantified the
uncertainties inherent in identified parameters. Yet, routinely estimating the
posterior distribution for all BC parameters in 3D simulations has been
unattainable due to the infeasible computational demand. We propose an
efficient method to identify Windkessel parameter posteriors using results from
a single high-fidelity three-dimensional (3D) model evaluation. We only
evaluate the 3D model once for an initial choice of BCs and use the result to
create a highly accurate zero-dimensional (0D) surrogate. We then perform
Sequential Monte Carlo (SMC) using the optimized 0D model to derive the
high-dimensional Windkessel BC posterior distribution. We validate this
approach in a publicly available dataset of N=72 subject-specific vascular
models. We found that optimizing 0D models to match 3D data a priori lowered
their median approximation error by nearly one order of magnitude. In a subset
of models, we confirm that the optimized 0D models still generalize to a wide
range of BCs. Finally, we present the high-dimensional Windkessel parameter
posterior for different measured signal-to-noise ratios in a vascular model
using SMC. We further validate that the 0D-derived posterior is a good
approximation of the 3D posterior. The minimal computational demand of our
method using a single 3D simulation, combined with the open-source nature of
all software and data used in this work, will increase access and efficiency of
Bayesian Windkessel calibration in cardiovascular fluid dynamics simulations.
