Uncovering near-wall blood flow from sparse data with physics-informed neural networks
Uncovering near-wall blood flow from sparse data with physics-informed neural networks
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DOI：
10.1063/5.0055600
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发表时间：
2021-07-01
影响因子：
4.6
通讯作者：
D'Souza, Roshan M.
中科院分区：
文献类型：
--
作者：
Arzani, Amirhossein;Wang, Jian-Xun;D'Souza, Roshan M.
近壁血流和壁切应力（WSS）调节主要形式的心血管疾病，但高保真度量化它们具有挑战性。WSS的患者特异性计算和实验测量遭受不确定性、低分辨率和噪声问题。物理信息神经网络（PINN）提供了一个灵活的深度学习框架，可以将控制血流的数学方程与测量数据集成在一起。通过利用关于控制方程（本文中为Navier-Stokes方程）的知识，PINN克服了深度学习中的大数据需求。在这项研究中，它显示了如何PINN可以用来改善WSS量化病变动脉血流。具体而言，血液流动的问题，进口和出口边界条件是未知的，解决了同化非常少的测量点。边界条件的不确定性是特定于患者的计算流体动力学模型的常见特征。结果表明，PINN可以使用稀疏的速度测量远离壁量化WSS具有非常高的精度，即使没有充分的知识的边界条件。考虑了理想化狭窄和动脉瘤模型中的示例，以证明如何将有关流动物理学的部分知识与部分测量相结合，以获得准确的近壁血流数据。所提出的混合数据驱动和基于物理的深度学习框架在转变心血管疾病的高保真近壁血流动力学建模方面具有很高的潜力。由AIP Publishing独家授权出版。
Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular disease, yet they are challenging to quantify with high fidelity. Patient-specific computational and experimental measurement of WSS suffers from uncertainty, low resolution, and noise issues. Physics-informed neural networks (PINNs) provide a flexible deep learning framework to integrate mathematical equations governing blood flow with measurement data. By leveraging knowledge about the governing equations (herein, Navier-Stokes), PINN overcomes the large data requirement in deep learning. In this study, it was shown how PINN could be used to improve WSS quantification in diseased arterial flows. Specifically, blood flow problems where the inlet and outlet boundary conditions were not known were solved by assimilating very few measurement points. Uncertainty in boundary conditions is a common feature in patient-specific computational fluid dynamics models. It was shown that PINN could use sparse velocity measurements away from the wall to quantify WSS with very high accuracy even without full knowledge of the boundary conditions. Examples in idealized stenosis and aneurysm models were considered demonstrating how partial knowledge about the flow physics could be combined with partial measurements to obtain accurate near-wall blood flow data. The proposed hybrid data-driven and physics-based deep learning framework has high potential in transforming high-fidelity near-wall hemodynamics modeling in cardiovascular disease. Published under an exclusive license by AIP Publishing.