Аннотация
This paper presents a novel hybrid approach that integrates computational fluid dynamics (CFD), physics-informed neural networks (PINN), and fluid–structure interaction (FSI) methods to simulate fluid flow in stenotic coronary artery trees and predict fractional flow reserve (FFR) in areas of stenosis. The primary objective is to utilize a 1D PINN model to accurately predict outlet flow conditions, effectively addressing the challenges of measuring or estimating these conditions within complex arterial networks. Validation against traditional CFD methods demonstrates strong accuracy while embedding physics-based training to ensure compliance with fundamental fluid dynamics principles. The findings indicate that the hybrid CFD PINN FSI method generates realistic outflow boundary conditions crucial for diagnosing stenosis, requiring minimal input data. By seamlessly integrating initial conditions established by the 1D PINN into FSI simulations, this approach enables precise assessments of blood flow dynamics and FFR values in stenotic regions. This innovative application of 1D PINN not only distinguishes this methodology from conventional data-driven models that rely heavily on extensive datasets but also highlights its potential to enhance our understanding of hemodynamics in pathological states. Ultimately, this research paves the way for significant advancements in non-invasive diagnostic techniques in cardiology, improving clinical decision making and patient outcomes.