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Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation

arXiv · Artificial Intelligence · article · Sep 16, 2026 · UTC

Clinical decision-making for coronary intervention relies mainly on angiography and fractional flow reserve (FFR). However, angiography is two-dimensional and lacks depth information for 3D lesion characterization, while FFR provides only a single functional index, offering limited hemodynamic insight. Among existing methods, numerical analysis is computationally expensive, whereas learning-based approaches require extensive supervision and often lack physical consistency. To address these limitations, we propose physics-informed hemodynamic modeling, an integrated deep learning framework for

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