Huxley-type muscle models offer a physiologically grounded description of cardiac contraction but remain computationally prohibitive for large-scale, multi-scale simulations. This article reviews surrogate modeling strategies that alleviate these costs for ventricular biomechanics, with emphasis on data-driven (RNN/TCN/GRU) and physics-informed (PINN) formulations and their coupling to finite-element solvers. The data-driven approach utilizes deep neural networks trained on numerical simulation data to replicate the behavior of the Huxley model while significantly reducing processing costs. The physics-informed approach approximates solutions to Huxley’s muscle contraction equation, which governs cross-bridge dynamics and force generation. By predicting the probability of myosin-actin interactions, this method enables direct calculation of stress and stiffness for finite element simulations. The coupling of these surrogate models with finite element computational frameworks allows for faster and more scalable simulations. Our goal is to provide a consolidated reference and actionable guidance for selecting and implementing surrogate approaches for Huxley-type muscle simulations.
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