A Systematic Review and Numerical Evaluation on Physics‐Informed Neural Network for Blood Flow Dynamics

Abstract

Physics‐Informed Neural Networks (PINNs) are deep neural networks that incorporate partial differential equations in their loss function to understand the physics of the system. Thereby enhancing the learning process of the model to achieve better predictive results than the existing models. PINNs embed governing physical principles directly into their loss functions, enabling them to solve complex problems even with limited or noisy data. This distinctive capability allows PINNs to effectively handle complex computational and engineering problems. Such problems include fluid flow prediction, heat transfer simulation, and wave propagation analysis. Hence, a comprehensive analysis of PINNs and their variations for fluid dynamics in the human biological system will be presented in this work. The findings of this numerical evaluation indicate that PINN‐based models are always in agreement with reference and in vivo data, which proves to be more accurate in predicting the major hemodynamic parameters, including velocity, pressure, and wall shear stress (WSS). However, challenges including high computational cost, convergence instability, and limitations in multi‐scale generalization remain significant. Further, the review addresses the foundational architecture of PINNs, improvements introduced through various extensions, and their relative advantages in different biofluid applications. Special emphasis will be placed on their applications in hemodynamics modeling blood flow dynamics and vascular biomechanics using PINNs. This study also discusses the major challenges of PINNs, such as high computational cost, convergence issues, multi‐scale modeling, and limited generalization. It also reviews proposed solutions and suggests future directions, including integration with other machine learning models for improved scalability and broader interdisciplinary applications.