[2603.19331] FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions

Computer Science > Machine Learning arXiv:2603.19331 (cs) [Submitted on 18 Mar 2026] Title:FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions Authors:Chloe H. Choi, Alison L. Marsden, Daniele E. Schiavazzi View a PDF of the paper titled FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions, by Chloe H. Choi and 2 other authors View PDF HTML (experimental) Abstract:Boundary condition tuning is a fundamental step in patient-specific cardiovascular modeling. Despite an increase in offline training cost, recent methods in data-driven variational inference can efficiently estimate the joint posterior distribution of boundary conditions, with amortization of training efforts over clinical targets. However, even the most modern approaches fall short in two important scenarios: open-loop models with known mean flow and assumed waveform shapes, and anatomies affected by vascular lesions where segmentation influences the reachability of pressure or flow split targets. In both cases, boundary conditions cannot be tuned in isolation. We introduce a general amortized inference framework based on probabilistic flow that treats clinical targets, inflow features, and point cloud embeddings of patient-specific anatomies as either conditioning variables or quantities to be jointly estimated. We demonstrate the approach on two patient-specific models: an aorto-iliac bifurcation with varying ste...