发表机构
Princeton University; Brown University; Universidad Católica del Uruguay; Duke University; Lingnan University; Apple（普林斯顿大学; 布朗大学; 乌拉圭天主教大学; 杜克大学; 岭南大学; 苹果公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于分叉的域分解方法，结合神经算子快速模拟血管网络血流，实现13-17倍加速且误差约1%，并泛化至不同拓扑。
AI 中文摘要
血管网络内血流动力学行为的快速准确模拟对于众多临床应用至关重要。然而，在复杂血管网络上获得高质量且计算高效的流量测量仍然具有挑战性。为解决这一问题，我们首先将血管网络分解为一组分叉单元，然后开发一个算子网络，能够将单元特定参数映射到每个分叉单元的局部解场。通过集中Windkessel模型出口参数并整合来自父单元解入口边界条件，可以快速近似流量和压力场。随后，在分叉单元之间应用算子网络驱动的Schwarz波形松弛方法，以修正不连续性并提高数值精度。在7段和55段动脉树模型上，所提方法相较于传统一维数值模拟实现了13倍至17倍的墙钟加速，压力和速度的相对L2误差均为1%。所得脉搏波速度生物标志物与传统参考值相差在1%至2%以内，且训练好的同一算子可泛化到不同的树状一维血管网络拓扑结构。
英文摘要
Fast and accurate simulation of hemodynamic behavior within vascular networks is essential for numerous clinical applications. However, obtaining high-quality and computationally efficient flow measurements across complex vascular networks remains challenging. To address this, we first decompose the vascular network into a set of bifurcation units and then develop an operator network capable of mapping unit-specific parameters to the local solution fields of each bifurcation unit. By lumping the Windkessel-model outlet parameters and incorporating inlet boundary conditions from the solution of parent units, the flow and pressure fields can be rapidly approximated. Subsequently, operator-network-driven Schwarz waveform relaxation is applied across bifurcation units to correct discontinuities and improve numerical accuracy. On 7-segment and 55-segment arterial tree models, the proposed method achieves $13\times$ to $17\times$ wall-clock speedups over conventional 1D numerical simulation, with relative $L^2$ errors of 1% in both pressure and velocity. The resulting pulse wave velocity biomarkers agree with the conventional reference to within 1--2%, and the same trained operator generalizes to different tree-like 1D vascular network topologies.
CommentsAccepted for publication in Multiscale Modeling & Simulation (SIAM)
