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

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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 paired with the corresponding information on boundary conditions and optimization settings are generated at low (32 x 32) and high (128 x 128) resolutions. To construct the artificial neural network for the proposed method, a convolutional neural network (CNN)-based encoder and decoder network is trained using the training dataset generated at low resolution. Then, as a two-stage refinement, the conditional generative adversarial network (cGAN) is trained with the optimized structures paired at both low and high resolutions and is connected to the trained CNN-based encoder and decoder network. The performance evaluation results of the integrated network demonstrate that the proposed method can determine a near-optimal structure in terms of pixel values and compliance with negligible computational time.
Publisher
SPRINGER
Issue Date
2019-03
Language
English
Article Type
Article
Citation

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, v.59, no.3, pp.787 - 799

ISSN
1615-147X
DOI
10.1007/s00158-018-2101-5
URI
http://hdl.handle.net/10203/254134
Appears in Collection
GT-Journal Papers(저널논문)
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