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：本研究将物理信息神经网络（PINN）模型应用于固体力学中的动态问题，重点探讨正问题与逆问题。我们特别展示了PINN模型如何在动态场景下高效实现材料参数识别。研究基于线性连续弹性理论，首先针对二维平面应变问题进行验证，随后将相同技术推广至三维问题。训练数据采用有限元法求解结果。严格验证表明：PINN模型兼具精确性、稳健性与计算高效性，尤其在作为材料识别问题的代理模型时优势显著。此外，我们采用了PINN领域先进技术对基础PINN实现进行改进。基于研究结果，本框架可便捷适配至求解固体力学中多种动态问题的计算平台。