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

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.

References in corpus (20)

• DeepXDE: A deep learning library for solving differential equations
• NSFnets (Navier-Stokes Flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
• Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data
• A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
• Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
• PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain
• Data-Driven Sparse Sensor Placement for Reconstruction
• Data-driven prediction of unsteady flow fields over a circular cylinder using deep learning
• Machine learning based spatio-temporal super resolution reconstruction of turbulent flows
• Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
• The Neural Particle Method -- An Updated Lagrangian Physics Informed Neural Network for Computational Fluid Dynamics
• From coarse wall measurements to turbulent velocity fields through deep learning
• When and why PINNs fail to train: A neural tangent kernel perspective
• Learning Unknown Physics of non-Newtonian Fluids
• Data-driven cardiovascular flow modeling: examples and opportunities
• Leveraging reduced-order models for state estimation using deep learning
• Physics-aware deep neural networks for surrogate modeling of turbulent natural convection
• Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks
• A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems
• Physics Informed Deep Learning for Flow and Transport in Porous Media

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