Automated Tuning of Cardiovascular Boundary Conditions via Differentiable Surrogate Modeling.

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.