Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics.
Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics.
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DOI：
10.1007/s10483-023-2995-8
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发表时间：
2023-07
影响因子：
4.4
通讯作者：
Lu, L.
中科院分区：
文献类型：
--
作者：
Wu, W.;Daneker, M.;Jolley, M. A.;Turner, K. T.;Lu, L.
关键词：
材料识别对于理解机械性能和相关机械功能之间的关系至关重要。然而，材料识别是一项具有挑战性的任务，特别是当材料的特性在本质上是高度非线性的，正如在生物组织中常见的那样。在这项工作中，我们通过物理信息神经网络（pinn）识别连续固体力学中未知的材料特性。为了提高pinn的精度和效率，我们开发了针对非均匀采样观测数据的有效策略。我们还研究了将dirichlet型边界条件（bc）作为软约束或硬约束的不同方法。最后，我们将提出的方法应用于跨越线弹性和超弹性材料空间的各种时间依赖和时间独立的固体力学实例。估计的材料参数相对误差小于1%。因此，这项工作与各种应用相关，包括优化结构完整性和开发新材料。
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 develop efficient strategies to nonuniformly sample observational data. We also investigate different approaches to enforce Dirichlet-type boundary conditions (BCs) 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.
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作者：
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