Energy-based physics-informed neural network for frictionless contact problems under large deformation
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910805517139968 |
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| author | Bai, Jinshuai Lin, Zhongya Wang, Yizheng Wen, Jiancong Liu, Yinghua Rabczuk, Timon Gu, YuanTong Feng, Xi-Qiao |
| author_facet | Bai, Jinshuai Lin, Zhongya Wang, Yizheng Wen, Jiancong Liu, Yinghua Rabczuk, Timon Gu, YuanTong Feng, Xi-Qiao |
| contents | 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) |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03671 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Energy-based physics-informed neural network for frictionless contact problems under large deformation Bai, Jinshuai Lin, Zhongya Wang, Yizheng Wen, Jiancong Liu, Yinghua Rabczuk, Timon Gu, YuanTong Feng, Xi-Qiao Computational Engineering, Finance, and Science Machine Learning Computational Physics 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) |
| title | Energy-based physics-informed neural network for frictionless contact problems under large deformation |
| topic | Computational Engineering, Finance, and Science Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2411.03671 |