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

Keyphrases

• Aneurysm Model 20%
• Arterial Flow 20%
• Blood Flow 100%
• Cardiovascular Disease 40%
• Computational Fluid Dynamics Modeling 20%
• Data Requirements 20%
• Deep Learning 20%
• Deep Learning Framework 40%
• Flow Data 20%
• Flow Physics 20%
• Flow Problems 20%
• Flow Shear Stress 20%
• Full Knowledge 20%
• Governing Equation 20%
• Hemodynamic Modeling 20%
• Hybrid Data 20%
• Inlet Boundary Condition 20%
• Leveraging Knowledge 20%
• Low Noise 20%
• Mathematical Equations 20%
• Measure Data 20%
• Navier-Stokes 20%
• Near-wall 100%
• Outlet Boundary Conditions 20%
• Partial Knowledge 20%
• Partial Measurements 20%
• Patient-specific 20%
• Patient-specific Computational Fluid Dynamics 20%
• Physics-informed Deep Learning 20%
• Physics-informed Neural Networks 100%
• Sparse Data 100%
• Stenosis Model 20%
• Stress Quantification 20%
• Velocity Measurement 20%
• Wall Shear Stress 80%

Engineering

• Boundary Condition 75%
• Computational Fluid Dynamics 25%
• Data Flow 25%
• Deep Learning 75%
• Dynamic Models 25%
• Experimental Measurement 25%
• Flow Physic 25%
• Flow Problem 25%
• Mathematical Equation 25%
• Measurement Data 25%
• Measurement Point 25%
• Outlet Boundary 25%
• Patient Specific 50%
• Wall Shear Stress 100%

Chemical Engineering

• Deep Learning 60%
• Neural Network 100%
• Velocity Measurement 20%