用能量模型解决大变形无摩擦接触问题，精度高且计算快。
Energy-based physics-informed neural network for frictionless contact problems under large deformation
- 基于能量法构建物理约束的神经网络框架
- 大变形下接触问题求解精度高，计算效率媲美商用FEM软件
- 适合复杂非线性接触建模，尤其工程仿真场景
接触力学的数值方法在工程应用中至关重要，可预测和分析复杂表面在多种条件下的相互作用。本文提出一种基于能量的物理信息神经网络（PINNs）框架，用于求解大变形下的无摩擦接触问题。受微观Lennard-Jones势启发，引入表面接触能描述接触现象。为确保框架鲁棒性，采用松弛、逐步加载和输出缩放技术。数值实验中，对经典的赫兹接触基准问题进行了验证，展示了所提PINNs框架的有效性和鲁棒性。此外，还测试了考虑几何与材料非线性的挑战性接触问题。结果表明，该框架为非线性接触力学提供了一种可靠且强大的工具。更重要的是，在处理复杂接触问题时，其计算效率与商业FEM软件相当。本文代码将在接受后公开于https://github.com/JinshuaiBai/energy_PINN_Contact。
原文摘要 · Abstract (English)
Numerical methods for contact mechanics are of great importance in engineering applications, enabling the prediction and analysis of complex surface interactions under various conditions. In this work, we propose an energy-based physics-informed neural network (PINNs) framework for solving frictionless contact problems under large deformation. Inspired by microscopic Lennard-Jones potential, a surface contact energy is used to describe the contact phenomena. To ensure the robustness of the proposed PINN framework, relaxation, gradual loading and output scaling techniques are introduced. In the numerical examples, the well-known Hertz contact benchmark problem is conducted, demonstrating the effectiveness and robustness of the proposed PINNs framework. Moreover, challenging contact problems with the consideration of geometrical and material nonlinearities are tested. It has been shown that the proposed PINNs framework provides a reliable and powerful tool for nonlinear contact mechanics. More importantly, the proposed PINNs framework exhibits competitive computational efficiency to the commercial FEM software when dealing with those complex contact problems. The codes used in this manuscript are available at https://github.com/JinshuaiBai/energy_PINN_Contact.(The code will be available after acceptance)
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。