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

This paper addresses clinical decision-making in coronary intervention by proposing a physics-informed hemodynamic modeling framework. The approach enables data-free prediction and sparse-data assimilation without relying on extensive supervision. It tackles the limitations of two-dimensional angiography and single-index FFR measurements by incorporating physical consistency into the learning process.