ArticleAnnals of biomedical engineering2026
Automated Tuning of Cardiovascular Boundary Conditions via Differentiable Surrogate Modeling.
Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
We present an end-to-end differentiable framework that facilitates three core capabilities in cardiovascular simulations: hemodynamic surrogate modeling, automated model calibration, and stochastic parameter tuning. Central to this approach is the introduction of a hybrid mechanistic and data-driven reduced order model (ROM) that represents each vascular domain through a nonlinear parametrization of lumped parameter networks. By exploiting the native differentiability of the pipeline, we calibrate the ROM parameters against a single high-fidelity 3D CFD simulation. The resulting optimized ROM serves as an efficient surrogate for both gradient-based deterministic and gradient-informed stochastic boundary condition calibration. With its computational efficiency and high fidelity, the framework directly addresses critical bottlenecks that currently limit the clinical adoption of cardiovascular simulations.
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