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|ㆍ Title||Y. G. Yu, T. I. Hur, J. H. Jung and I. G. Jang, "Deep learning for determining a near-optimal topological design without any iteration", Struct. Multidisc. Optim., vol. 59, no.3, pp. 787-799, 2019.|
|ㆍ Hits||509||ㆍ Date||2019-02-12 20:07|
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.
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