Uncovering near-wall blood flow from sparse data with physics-informed neural networks

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 ne...

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Veröffentlicht in:	Physics of fluids (1994) 2021-07, Vol.33 (7)
Hauptverfasser:	Arzani, Amirhossein, Wang, Jian-Xun, D'Souza, Roshan M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Blood flow Boundary conditions Cardiovascular disease Computational fluid dynamics Deep learning Fluid dynamics Hemodynamics Mathematical models Neural networks Physics Uncertainty Wall shear stresses
Online-Zugang:	Volltext
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