Identifying constitutive parameters in engineering and biological materials, particularly those with intricate geometries and mechanical behaviors, remains a longstanding challenge. The recent advent of Physics-Informed Neural Networks (PINNs) offers promising solutions, but current frameworks are often limited to basic constitutive laws and encounter practical constraints when combined with experimental data. In this paper, we introduce a robust PINN-based framework designed to identify material parameters for soft materials, specifically those exhibiting complex constitutive behaviors, under large deformation in plane stress conditions. Distinctively, our model emphasizes training PINNs with multi-modal synthetic experimental datasets consisting of full-field deformation and loading history, ensuring algorithm robustness even with noisy data. Our results reveal that the PINNs framework can accurately identify constitutive parameters of the incompressible Arruda-Boyce model for samples with intricate geometries, maintaining an error below 5%, even with an experimental noise level of 5%. We believe our framework provides a robust modulus identification approach for complex solids, especially for those with geometrical and constitutive complexity.

翻译：在工程与生物材料中，尤其对于具有复杂几何构型及力学行为的材料，其本构参数的辨识始终是一项长期挑战。近期兴起的物理信息神经网络（PINNs）为此提供了极具前景的解决方案，然而现有框架通常局限于基础本构定律，且在与实验数据结合时面临实际限制。本文提出一种基于PINNs的稳健框架，专门用于识别软材料（尤其是展现复杂本构行为的材料）在平面应力条件下大变形时的材料参数。该方法独特之处在于强调利用多模态合成实验数据集（涵盖全场变形与加载历程）训练PINNs，从而确保算法对含噪数据的鲁棒性。结果表明，PINNs框架能够精确辨识具有复杂几何构型样本的非压缩性Arruda-Boyce模型本构参数，即便在5%的噪声水平下，辨识误差仍低于5%。我们相信，本框架为几何及本构复杂性高的固体材料提供了一种稳健的模量识别方法。