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.
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Uncovering near-wall blood flow from sparse data with physics-informed neural networks
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July 2021
Research Article|
July 12 2021
Uncovering near-wall blood flow from sparse data with physics-informed neural networks
Amirhossein Arzani
;
Amirhossein Arzani
a)
1
Department of Mechanical Engineering, Northern Arizona University
, Flagstaff, Arizona 86011, USA
a)Author to whom correspondence should be addressed: [email protected]
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Jian-Xun Wang
;
Jian-Xun Wang
2
Department of Aerospace and Mechanical Engineering, University of Notre Dame
, Notre Dame, Indiana 46556, USA
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Roshan M. D'Souza
Roshan M. D'Souza
3
Department of Mechanical Engineering, University of Wisconsin–Milwaukee
, Milwaukee, Wisconsin 53211, USA
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a)Author to whom correspondence should be addressed: [email protected]
Physics of Fluids 33, 071905 (2021)
Article history
Received:
April 30 2021
Accepted:
June 21 2021
Citation
Amirhossein Arzani, Jian-Xun Wang, Roshan M. D'Souza; Uncovering near-wall blood flow from sparse data with physics-informed neural networks. Physics of Fluids 1 July 2021; 33 (7): 071905. https://doi.org/10.1063/5.0055600
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