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| Physics-informed neural networks for solving steady/transient heat conduction problems of functionally graded materials |
| Received:December 29, 2021 Revised:July 12, 2022 |
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| DOI：10.7511/jslx20211229002 |
| KeyWord:physical-informed neural networks extended physics-informed neural networks functionally graded materials heat conduction analysis |
| Author | Institution |
| 余波 |
合肥工业大学 土木与水利工程学院, 工程力学系, 合肥 ;大连理工大学 工业装备结构分析优化与CAE软件全国重点实验室, 大连 |
| 许梦强 |
合肥工业大学 土木与水利工程学院, 工程力学系, 合肥 |
| 高强 |
大连理工大学 工业装备结构分析优化与CAE软件全国重点实验室, 大连 |
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| Abstract: |
| In this paper,the physical-informed Neural Networks (PINN) was used to solve steady and transient heat conduction problems in Functionally Graded Materials (FGMs).The loss function in PINN is established by using the residuals of the governing equations,initial and boundary conditions,so that the neural network model with more generalization ability can be obtained even without any response data.In addition,the necessary work in traditional numerical methods such as differential and integral formula derivation,heavy modeling and meshing can be avoided when computational mechanics problems are solved.The applicability of PINN and extended physical-informed Neural Networks (XPINN) are investigated for solving steady and transient heat conduction in FGMs.Moreover,the complexity of network structures is discussed.The numerical results show that PINN and XPINN have high reliability and simple solution process for steady and transient heat conduction in FGMs when the geometry of the model is complex.In addition,this work provides a new way for solving complex multi-physical fields coupling and inclusion problems in extreme environments. |
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