Computational fluid dynamics is a common tool in cardiovascular science and engineering to simulate, predict and study hemodynamics in arteries. However, owing to the complexity and scale of cardiovascular flow problems, the evaluation of the model could be computationally expensive, especially in those cases where a large number of evaluations are required, such as uncertainty quantification and design optimisation. In such scenarios, the model may have to be repeatedly evaluated due to the changes or distinctions of simulation domains. In this work, a data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. The proposed surrogate model leverages surface registration to parameterise those similar but distinct shapes and formulate corresponding hemodynamics information into geometry-informed snapshots by the diffeomorphism constructed between the reference domain and target domain. A non-intrusive reduced-order model for geometrical parameters is subsequently constructed using proper orthogonal decomposition, and a radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model based on reduced coefficients of geometrical parameters of the shape. Two examples of blood flowing through a stenosis and a bifurcation are presented and analysed. The proposed surrogate model demonstrates its accuracy and efficiency in hemodynamics prediction and shows its potential application toward real-time simulation or uncertainty quantification for complex patient-specific scenarios.

翻译：计算流体动力学是心血管科学与工程中模拟、预测和研究动脉血流动力学的常用工具。然而，受心血管流动问题的复杂性和规模影响，模型评估的计算成本可能较高，尤其在需要大量评估的场景中（如不确定性量化和设计优化）。在此类情况下，由于模拟域的变化或差异，模型可能需要反复评估。本研究提出一种数据驱动代理模型，用于高效预测相似但不同域上的血流模拟。该代理模型利用表面配准对相似但不同的形状进行参数化，并通过参考域与目标域之间构建的微分同胚，将相应的血流动力学信息整合为几何信息快照。随后，采用本征正交分解构建几何参数的非侵入式降阶模型，并训练径向基函数插值器，基于形状几何参数的降阶系数预测降阶模型的降阶系数。本文以血液流经狭窄和分叉两种案例进行分析验证。该代理模型在血流动力学预测中展现出精确性与高效性，并显示出其在针对复杂患者特定场景的实时模拟或不确定性量化中的潜在应用价值。