Automated Tuning of Cardiovascular Boundary Conditions via Differentiable Surrogate Modeling.

Authors

Shridhar Thakar, Mehran Mirramezani

Published in

Annals of biomedical engineering. Jul 21, 2026. Epub Jul 21, 2026.

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.

PMID:
42479367
Bibliographic data and abstract were imported from PubMed on 21 Jul 2026.

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