Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience reduced accuracy when working with anatomies that contain numerous junctions or pathological conditions. We develop one-dimensional reduced-order models that simulate blood flow dynamics using a graph neural network trained on three-dimensional hemodynamic simulation data. Given the initial condition of the system, the network iteratively predicts the pressure and flow rate at the vessel centerline nodes. Our numerical results demonstrate the accuracy and generalizability of our method in physiological geometries comprising a variety of anatomies and boundary conditions. Our findings demonstrate that our approach can achieve errors below 2% and 3% for pressure and flow rate, respectively, provided there is adequate training data. As a result, our method exhibits superior performance compared to physics-based one-dimensional models, while maintaining high efficiency at inference time.

翻译：基于物理的降阶模型因其高效性成为心血管建模中的常用选择，但在处理含大量分叉或病理状态的解剖结构时，其精度可能下降。我们开发了一种一维降阶模型，该模型利用基于三维血流动力学仿真数据训练的图神经网络模拟血流动力学。在给定系统初始条件后，网络迭代预测血管中心线节点处的压力和流量。数值结果表明，该方法在包含多种解剖结构和边界条件的生理几何构型中具有准确性和泛化能力。研究发现，在训练数据充足的情况下，该方法可分别实现压力误差低于2%、流量误差低于3%。因此，该方法在保持推理时高效性的同时，表现优于基于物理的一维模型。