用降维方法加速脑血管血流模拟，精度与速度兼备。
Model Order Reduction of Cerebrovascular Hemodynamics Using POD_Galerkin and Reservoir Computing_based Approach
- 用POD压缩高保真仿真数据，构建低维表示空间。
- 两类降维模型提速100到1000倍，预测壁面剪切应力准确。
- 适合需要快速仿真的医学建模与临床辅助决策场景。
针对脑血管系统中非定常血流的模拟，本文比较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的3D计算流体动力学（CFD）快照压缩至低维隐空间。评估了投影纳维-斯托克斯方程的POD-Galerkin（POD-G）模型，以及通过循环神经网络学习系数时序演化的POD-Reservoir Computing（POD-RC）模型。引入多谐波、多幅值训练信号以提升训练效率。两类方法相较全阶模拟均实现10²至10³倍的计算加速，验证了其作为预测壁面剪切应力等流场量的高效且精确代理模型的潜力。
原文摘要 · Abstract (English)
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.
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