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:物理启发式神经网络(PINN)在计算力学中越来越受到关注。本研究详细介绍了基于PINN的计算固体力学的编程方式。此外,总结了两种广泛使用的用于基于PINN的计算固体力学的物理启发式损失函数。 此外,提供了从一维到三维固体问题的数值例子,以显示基于PINN的计算固体力学的性能。该程序通过Python编程语言和TensorFlow库构建,具有逐步说明。值得强调的是,基于PINN的计算力学易于实现,并可扩展到更具挑战性的应用程序。 本研究旨在帮助那些对基于PINN的固体力学求解器感兴趣的研究人员清晰了解这一新兴领域。本研究所提供的所有数值例子的程序都可以在 https://github.com/JinshuaiBai/PINN_Comp_Mech 中获得。