Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning

In this paper, we present a deep autoencoder based energy method (DAEM) for the bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher order continuity of the DAEM and integrates a deep autoencoder and the minimum total potential principle in one framework yieldin...

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Veröffentlicht in:	European journal of mechanics, A, Solids Jg. 87; S. 104225
Hauptverfasser:	Zhuang, Xiaoying, Guo, Hongwei, Alajlan, Naif, Zhu, Hehua, Rabczuk, Timon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin Elsevier Masson SAS 01.05.2021 Elsevier BV
Schlagworte:	Activation function Artificial neural networks Autoencoder Bending Boundary conditions Buckling Configuration management Continuity (mathematics) Deep learning Energy method Energy methods Feature extraction Hyperbolic functions Kirchhoff plate Machine learning Plates Potential energy Resonant frequencies Robustness (mathematics) Transfer learning Vibration Vibration analysis Deep learning Energy method Vibration Autoencoder Activation function Transfer learning Kirchhoff plate Buckling
ISSN:	0997-7538, 1873-7285
Online-Zugang:	Volltext
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