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<p>We present an end-to-end <i>differentiable</i> 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.</p>

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Automated Tuning of Cardiovascular Boundary Conditions via Differentiable Surrogate Modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Shridhar Thakar,
  • Mehran Mirramezani

摘要

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.