Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs). To improve the accuracy and efficiency of PINNs, we developed efficient strategies to nonuniformly sample observational data. We also investigated different approaches to enforce Dirichlet boundary conditions as soft or hard constraints. Finally, we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space. The estimated material parameters achieve relative errors of less than 1%. As such, this work is relevant to diverse applications, including optimizing structural integrity and developing novel materials.

翻译：材料识别对于理解机械特性和相关机械功能之间的关系至关重要。然而,材料识别是一项艰巨的任务,特别是当材料的特性在生物组织中非常非线性时更是如此。在这项工作中,我们通过物理知情神经网络(PINNs)确定连续固态力学中未知的物质特性。为了提高PINNs的准确性和效率,我们制定了高效的战略,以不统一的样本观测数据。我们还调查了将Drichlet边界条件作为软性或硬性限制来强制实施的不同方法。最后,我们将拟议方法应用于一系列不同、有时间依赖性和时间依赖性的固体机械学实例,这些实例涉及线性弹性和超弹性材料空间。估计材料参数的相对误差不到1%。因此,这项工作与多种应用有关,包括优化结构完整性和开发新材料。