Bayesian Windkessel calibration using optimized 0D surrogate models
<|reference_start|>Bayesian Windkessel calibration using optimized 0D surrogate models: 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.<|reference_end|>
@article{richter2024bayesian,
    title={Bayesian Windkessel calibration using optimized 0D surrogate models},
    author={Jakob Richter, Jonas Nitzler, Luca Pegolotti, Karthik Menon, Jonas
  Biehler, Wolfgang A. Wall, Daniele E. Schiavazzi, Alison L. Marsden, Martin
  R. Pfaller},
    journal={arXiv preprint arXiv:2404.14187},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14187},
    primaryClass={cs.CE}
}