AI-Based Physics Geometry Adaptation Framework for Accurate and Generalizable Hemodynamic Modeling

Three-dimensional Hemodynamic modeling and application demonstrate significant value. In the paper, we propose a 3D hemodynamic computation framework (PGAF) integrating variational inference and optimal transport theory. Variational inference enables adaptive inference of reasonable initial solutions for unseen cases by linking vascular features and boundary conditions with latent hemodynamic patterns. Optimal transport theory constructs a metric between predicted flow fields and established hemodynamic solution space, which is established through analysis of large-scale computational fluid dynamics (CFD) simulation data, to constrain global physical deviation. PGAF implements a dynamic coupling correction mechanism where the hemodynamic results are iteratively refined through the optimization，so it ensures global physical consistency in vessels and calculation accuracy during evolution. We collect 2500 different vessels from 7 public image datasets and perform 8000 CFD simulations under various boundary conditions. PGAF is ultimately validated and evaluated in 500 cases containing invasive catheter measurements. The results demonstrate the ability of our method to accurately predict the velocity and pressure distributions within the vessels, demonstrating high generalizability, robustness, and precision under different boundary conditions.