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| A physics-informed deep learning method for solving forward and inverse mechanics problems of thin rectangular plates |
| Received:November 10, 2020 Revised:December 05, 2020 |
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| DOI：10.7511/jslx20201110003 |
| KeyWord:physics-informed neural network deep learning thin rectangular plates forward and inverse mechanics problems |
| Author | Institution |
| 唐明健 |
同济大学 土木工程学院, 上海 |
| 唐和生 |
同济大学 土木工程学院, 上海 |
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| Abstract: |
| In this paper, a physics-informed neural network model is developed using a deep learning method to solve out both the forward and inverse mechanics problems of thin rectangular plates.In the forward mechanics problem, the basic parameters, boundary conditions and load distributions are known to solve the deflection.In the inverse problem, part of deflection and basic parameters, load distributions are known to identify the boundary conditions.In the physics-informed deep neural network, the loss function takes the basic bending equation and stress-strain constitutive relation of thin rectangular plates into consideration, apart from the deflection data fitting in the data-driven model.The results show the excellent predictive effect of the model.Furthermore, the comparison between physics-informed and data-driven neural networks indicates that the data-driven model needs a larger range of training data set, and its number of iterations is larger, while the physics-informed model can reduce the coverage of required data and improve computational efficiency while keeping a certain accuracy. |
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