Bayesian Windkessel calibration using optimized zero-dimensional surrogate models

Bayesian boundary condition (BC) calibration approaches from clinical measurements have successfully quantified inherent uncertainties in cardiovascular fluid dynamics simulations. However, estimating the posterior distribution for all BC parameters in three-dimensional (3D) simulations has been unattainable due to infeasible computational demand. We propose an efficient method to identify Windkessel parameter posteriors: 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. Optimizing 0D models to match 3D data a priori lowered their median approximation error by nearly one order of magnitude in 72 publicly available vascular models. The optimized 0D models generalized well to a wide range of BCs. Using SMC, we evaluated the high-dimensional Windkessel parameter posterior for different measured signal-to-noise ratios in a vascular model, which we validated against a 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.     «

Bayesian boundary condition (BC) calibration approaches from clinical measurements have successfully quantified inherent uncertainties in cardiovascular fluid dynamics simulations. However, estimating the posterior distribution for all BC parameters in three-dimensional (3D) simulations has been unattainable due to infeasible computational demand. We propose an efficient method to identify Windkessel parameter posteriors: We only evaluate the 3D model once for an initial choice of BCs and use th...     »