Deep learning for determining a near-optimal topological design without any iteration

In this study, we propose a novel deep learning-based method to predict an optimized structure for a given boundary condition and optimization setting without using any iterative scheme. For this purpose, first, using open-source topology optimization code, datasets of the optimized structures paire...

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Veröffentlicht in:	Structural and multidisciplinary optimization Jg. 59; H. 3; S. 787 - 799
Hauptverfasser:	Yu, Yonggyun, Hur, Taeil, Jung, Jaeho, Jang, In Gwun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2019 Springer Nature B.V
Schlagworte:	Artificial neural networks Boundary conditions Coders Computational Mathematics and Numerical Analysis Computing time Datasets Deep learning Engineering Engineering Design Generative adversarial networks Iterative methods Machine learning Neural networks Optimization Performance evaluation Research Paper Source code Theoretical and Applied Mechanics Topology optimization Deep learning Topology optimization Convolutional neural network Machine learning Generative adversarial network Generative model
ISSN:	1615-147X, 1615-1488
Online-Zugang:	Volltext
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