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Deep learning for determining a near-optimal topological design without any iteration.
- Source :
- Structural & Multidisciplinary Optimization; Mar2019, Vol. 59 Issue 3, p787-799, 13p
- Publication Year :
- 2019
-
Abstract
- 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 × 32) and high (128 × 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1615147X
- Volume :
- 59
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Structural & Multidisciplinary Optimization
- Publication Type :
- Academic Journal
- Accession number :
- 135412247
- Full Text :
- https://doi.org/10.1007/s00158-018-2101-5