In this work, we present the physics-informed neural network (PINN) model applied particularly to dynamic problems in solid mechanics. We focus on forward and inverse problems. Particularly, we show how a PINN model can be used efficiently for material identification in a dynamic setting. In this work, we assume linear continuum elasticity. We show results for two-dimensional (2D) plane strain problem and then we proceed to apply the same techniques for a three-dimensional (3D) problem. As for the training data we use the solution based on the finite element method. We rigorously show that PINN models are accurate, robust and computationally efficient, especially as a surrogate model for material identification problems. Also, we employ state-of-the-art techniques from the PINN literature which are an improvement to the vanilla implementation of PINN. Based on our results, we believe that the framework we have developed can be readily adapted to computational platforms for solving multiple dynamic problems in solid mechanics.

翻译：本文提出了一种专门应用于固体力学动态问题的物理信息神经网络（PINN）模型。我们聚焦于正问题和逆问题，重点展示了PINN模型在动态条件下如何高效用于材料识别。研究中采用了线性连续弹性理论，首先给出了二维（2D）平面应变问题的结果，随后将相同方法应用于三维（3D）问题。训练数据基于有限元方法获得的解。我们严谨证明了PINN模型具有高精度、鲁棒性和计算高效性，特别是在作为材料识别问题的替代模型时。此外，我们采用了PINN文献中的最新技术，这些技术对PINN的基础实现进行了改进。基于所得结果，我们认为所开发的框架可便捷地应用于固体力学中多个动态问题的求解计算平台。