TL;DR
A reproducible framework that links computational physics, biomedical modeling, and data-driven learning is established, offering a foundation for future clinical translation of PINN-based hemodynamic analysis.
It is highlighted that while current PINNs can reliably reproduce velocity fields, their ability to capture localized pressure dynamics remains limited, indicating the need for more robust formulations.
: Hemodynamic information is crucial for studying cardiovascular pathogenesis and risk assessment. When measurement data are sparse, boundary data are incomplete, and branching geometries are involved, classical computational fluid dynamics (CFD) remains computationally intensive, while standard physics-informed neural...
The proposed PINN framework demonstrates that PINNs can serve as an efficient and accurate alternative for modelling complex vascular flow phenomena, offering significant advantages in scalability and computational cost reduction while maintaining physical consistency.
An operator network capable of mapping unit-specific parameters to the local solution fields of each bifurcation unit is developed and the resulting pulse wave velocity biomarkers agree with the conventional reference to within 1--2%, and the same trained operator generalizes to different tree-like 1D vascular network...
The aim of this systematic review is to systematize the methodological approaches used in the state‐of‐the‐art literature, including the choice of turbulence models, validation methods, as well as the identification of the main hemodynamic parameters characterizing turbulent flow.
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