论文标题
编程物理知识的神经网络的计算固体力学简介
An introduction to programming Physics-Informed Neural Network-based computational solid mechanics
论文作者
论文摘要
物理信息神经网络（PINN）最近对计算力学的兴趣越来越大。在这项工作中，我们介绍了基于PINN的计算固体力学的详细介绍。此外，总结了基于PINN的两个广泛使用的物理信息损失功能。此外，提出了从1D到3D实心问题的数值示例，以显示基于PINN的计算固体力学的性能。这些程序是通过Python编码语言和带有分步说明的TensorFlow库构建的。值得强调的是，基于Pinn的计算机制易于实施，并且可以扩展到更具挑战性的应用程序。这项工作旨在帮助对基于Pinn的固体机械求解器感兴趣的研究人员对这一新兴领域有清晰的见解。本工作中提供的所有数值示例的程序可在https://github.com/jinshuaibai/pinn_comp_mech上获得。
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.
