Physics-informed neural network (PINN) has recently gained increasing interest in computational mechanics. In this work, we present a detailed introduction to programming PINN-based computational solid mechanics. Besides, two prevailingly used physics-informed loss functions for PINN-based computational solid mechanics are summarised. Moreover, numerical examples ranging from 1D to 3D solid problems are presented to show the performance of PINN-based computational solid mechanics. The programs are built via Python coding language and TensorFlow library with step-by-step explanations. It is worth highlighting that PINN-based computational mechanics is easy to implement and can be extended for more challenging applications. This work aims to help the researchers who are interested in the PINN-based solid mechanics solver to have a clear insight into this emerging area. The programs for all the numerical examples presented in this work are available on https://github.com/JinshuaiBai/PINN_Comp_Mech.

翻译：物理信息神经网络（PINN）近年来在计算力学领域日益受到关注。本文详细介绍了基于PINN的固体力学计算方法编程，归纳了该类方法中两种常用的物理信息损失函数，并通过一维至三维固体问题的数值算例展示了PINN固体力学计算方法的性能。所有程序均采用Python编程语言和TensorFlow库实现，并附有分步解释。值得强调的是，基于PINN的计算力学方法易于实现，并可扩展至更具挑战性的应用场景。本文旨在帮助对基于PINN的固体力学求解器感兴趣的学者深入理解这一新兴领域。本文所有数值算例的程序均可在https://github.com/JinshuaiBai/PINN_Comp_Mech 获取。