Cardiac muscle tissue exhibits highly non-linear hyperelastic and orthotropic material behavior during passive deformation. Traditional constitutive identification protocols therefore combine multiple loading modes and typically require multiple specimens and substantial handling. In soft living tissues, such protocols are challenged by inter- and intra-sample variability and by manipulation-induced alterations of mechanical response, which can bias inverse calibration. In this work we exploit spatially heterogeneous full-field kinematics as an information-rich alternative to multimodal testing. We adapt EUCLID, an unsupervised method for the automated discovery of constitutive models, towards Bayesian parameter inference for highly nonlinear, orthotropic constitutive models. Using synthetic myocardial tissue slabs, we demonstrate that a single heterogeneous biaxial experiment, combined with sparse reaction-force measurements, enables robust recovery of Holzapfel-Ogden parameters with quantified uncertainty, across multiple noise levels. The inferred responses agree closely with ground-truth simulations and yield credible intervals that reflect the impact of measurement noise on orthotropic material model inference. Our work supports single-shot, uncertainty-aware characterization of nonlinear orthotropic material models from a single biaxial test, reducing sample demand and experimental manipulation.

翻译：心肌组织在被动变形过程中表现出高度非线性超弹性与正交各向异性的材料特性。传统的本构建模识别方案因此需结合多种加载模式，通常要求多个样本及大量操作。对于软组织而言，此类方案受到样本间与样本内变异性的挑战，且操作引起的力学响应改变可能导致逆向校准产生偏差。本研究利用空间非均匀的全场运动学作为多模态测试的信息密集型替代方案。我们改进无监督本构模型自动发现方法EUCLID，使其适用于高度非线性正交各向异性本构模型的贝叶斯参数推断。通过合成心肌组织薄片实验，我们证明单次非均匀双轴试验结合稀疏反作用力测量，能够在多种噪声水平下实现Holzapfel-Ogden参数的稳健反演与量化不确定性评估。推断响应与真实仿真结果高度吻合，所得置信区间有效反映了测量噪声对正交各向异性材料模型推断的影响。本研究支持基于单次双轴试验实现非线性正交各向异性材料模型的单次测量不确定性量化表征，从而降低样本需求与实验操作复杂度。