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Modeling Geometrically Nonlinear FG Plates: A Fast and Accurate Alternative to IGA Method Based on Deep Learning

Se Li1, Tiantang Yu1,*, Tinh Quoc Bui2

1 College of Mechanics and Materials, Hohai University, Nanjing, 211100, China
2 Duy Tan Research Institute for Computational Engineering (DTRICE), Duy Tan University, Ho Chi Minh City, Vietnam

* Corresponding Author: Tiantang Yu. Email: email

(This article belongs to this Special Issue: Theoretical and Computational Modeling of Advanced Materials and Structures)

Computer Modeling in Engineering & Sciences 2024, 138(3), 2793-2808. https://doi.org/10.32604/cmes.2023.030278

Abstract

Isogeometric analysis (IGA) is known to show advanced features compared to traditional finite element approaches. Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functional grading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward a deep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complex IGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trained using the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationship between the outputs and the inputs is constructed using machine learning so that the displacements can be directly estimated by the deep learning network. To provide enough training data, we use S-FSDT Von-Karman IGA and obtain the displacement responses for different loads and gradient indexes. Results show that the recognition error is low, and demonstrate the feasibility of deep learning technique as a fast and accurate alternative to IGA for modeling the geometrically nonlinear bending behavior of FG plates.

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Cite This Article

Li, S., Yu, T., Bui, T. Q. (2024). Modeling Geometrically Nonlinear FG Plates: A Fast and Accurate Alternative to IGA Method Based on Deep Learning. CMES-Computer Modeling in Engineering & Sciences, 138(3), 2793–2808.



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