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.
Fast and accurate simulation of hemodynamic behavior within vascular networks is essential for numerous clinical applications. However, obtaining high-quality and computationally efficient flow measurements across complex vascular networks remains challenging. To address this, we first decompose the vascular network in...
Over the past decade, the development of accurate turbulence models to describe blood flow in the aorta for cardiovascular applications has increased substantially, leading to growing efforts to quantify turbulence‐related hemodynamic parameters and improved model fidelity. At the same time, a unified computational sta...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
