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

TL;DR

This paper demonstrates how physics-informed neural networks can accurately estimate near-wall blood flow and wall shear stress from sparse data, even with uncertain boundary conditions, improving cardiovascular disease modeling.

Contribution

It introduces a novel PINN-based method for quantifying wall shear stress using limited measurements and partial physics knowledge in complex arterial flows.

Findings

PINN accurately estimates WSS with sparse data.

Method handles unknown boundary conditions effectively.

Demonstrated on stenosis and aneurysm models.

Abstract

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 (PINN) 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.