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 recast 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呈现出高度非线性的超弹性和正交各向异性材料行为。传统的本构识别协议通常需要结合多种加载模式，并常需多个样本及大量操作。在软活体组织中，此类协议面临样本间、样本内变异及操作引起的力学响应改变等挑战，这些因素可能使反演校准产生偏差。本研究利用空间异质全场运动学作为多模态测试的信息丰富替代方案，将无监督本构模型自动发现方法EUCLID重构为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框架。通过合成心肌组织切片实验证明，结合稀疏反作用力测量，单次异质双轴试验即可在多种噪声水平下稳健恢复Holzapfel-Ogden参数并量化不确定性。推断响应与真实模拟结果高度吻合，其可信区间反映了测量噪声对正交各向异性材料模型推断的影响。本研究支持从单次双轴测试中实现非线性正交各向异性材料模型的一次性、含不确定度表征，从而减少样本需求与实验操作。