We investigate model order reduction (MOR) strategies for simulating unsteady hemodynamics within cerebrovascular systems, contrasting a physics-based intrusive approach with a data-driven non-intrusive framework. High-fidelity 3D Computational Fluid Dynamics (CFD) snapshots of an idealised basilar artery bifurcation are first compressed into a low-dimensional latent space using Proper Orthogonal Decomposition (POD). We evaluate the performance of a POD-Galerkin (POD-G) model, which projects the Navier-Stokes equations onto the reduced basis, against a POD-Reservoir Computing (POD-RC) model that learns the temporal evolution of coefficients through a recurrent architecture. A multi-harmonic and multi-amplitude training signal is introduced to improve training efficiency. Both methodologies achieve computational speed-ups on the order of 10^2 to 10^3 compared to full-order simulations, demonstrating their potential as efficient and accurate surrogates for predicting flow quantities such as wall shear stress.

翻译：本文研究了用于模拟脑血管系统非定常血液动力学的模型降阶（MOR）策略，对比了基于物理的侵入式方法与数据驱动的非侵入式框架。首先利用本征正交分解（POD）将理想化基底动脉分叉处的高保真三维计算流体动力学（CFD）快照压缩至低维潜空间。我们评估了POD-Galerkin（POD-G）模型（该模型将纳维-斯托克斯方程投影至降阶基）与POD-储层计算（POD-RC）模型（后者通过循环架构学习系数的时序演变）的性能表现。引入多谐波多振幅训练信号以提高训练效率。两种方法相较于全阶模拟均实现了10^2至10^3量级的计算加速，证明了其作为预测壁面剪切应力等流动量的高效准确替代模型的潜力。