Richter Jakob, Nitzler Jonas, Pegolotti Luca, Menon Karthik, Biehler Jonas, Wall Wolfgang A, Schiavazzi Daniele E, Marsden Alison L, Pfaller Martin R
Department of Pediatrics, Stanford University, Stanford, CA, USA.
Institute for Computational Mechanics, Technical University of Munich, München, Bayern, Germany.
Philos Trans A Math Phys Eng Sci. 2025 Mar 13;383(2292):20240223. doi: 10.1098/rsta.2024.0223.
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 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.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.
基于临床测量的贝叶斯边界条件（BC）校准方法已成功量化了心血管流体动力学模拟中的固有不确定性。然而，由于计算需求不可行，在三维（3D）模拟中估计所有BC参数的后验分布一直无法实现。我们提出了一种有效的方法来识别风箱参数后验：对于BC的初始选择，我们仅对3D模型进行一次评估，并使用结果创建一个高度准确的零维（0D）替代模型。然后，我们使用优化后的0D模型执行序贯蒙特卡罗（SMC），以得出高维风箱BC后验分布。在72个公开可用的血管模型中，将0D模型优化以匹配3D数据，使其中值近似误差降低了近一个数量级。优化后的0D模型能很好地推广到广泛的BC情况。使用SMC，我们在一个血管模型中评估了不同测量信噪比下的高维风箱参数后验，并与3D后验进行了验证。我们的方法仅使用一次3D模拟，计算需求极小，再加上本工作中使用的所有软件和数据的开源性质，将提高心血管流体动力学模拟中贝叶斯风箱校准的可及性和效率。本文是主题为“医疗保健和生物系统的不确定性量化（第1部分）”的一部分。
