Browse > Article
http://dx.doi.org/10.9712/KASS.2021.21.4.39

GAN-based Data Augmentation methods for Topology Optimization  

Lee, Seunghye (Dept. of Architectural Engineering, Sejong University)
Lee, Yujin (Dept. of Architectural Engineering, Sejong University)
Lee, Kihak (Dept. of Architectural Engineering, Sejong University)
Lee, Jaehong (Dept. of Architectural Engineering, Sejong University)
Publication Information
Journal of Korean Association for Spatial Structures / v.21, no.4, 2021 , pp. 39-48 More about this Journal
Abstract
In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.
Keywords
Ganerative Adversarial Network; Topology optimization; Data augmentation; Machine learning;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Rawat, S., & Shen, M. H. H., "A novel topology optimization approach using conditional deep learning", arXiv preprint arXiv:1901.04859, 2019
2 Oh, S., Jung, Y., Kim, S., Lee, I., & Kang, N., "Deep generative design: Integration of topology optimization and generative models", Journal of Mechanical Design, Vol.141, No.11, 2019
3 Bendsoe, M. P., & Sigmund, O., "Topology optimization: theory, methods, and applications", Springer Science & Business Media, 2013
4 M. Arjovsky, S. Chintala, and L. Bottou., "Wasserstein gan", arXiv preprint arXiv:1701.07875, 2017.
5 Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A., "Improved training of wasserstein gans", arXiv preprint arXiv:1704.00028, 2017
6 Radford, A., Metz, L., & Chintala, S., "Unsupervised representation learning with deep convolutional generative adversarial networks", arXiv preprint arXiv:1511.06434, 2015
7 Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., & Paul Smolley, S., "Least squares generative adversarial networks", In Proceedings of the IEEE international conference on computer vision, pp. 2794-2802, 2017
8 Sigmund, O., "A 99 line topology optimization code written in Matlab", Structural and multidisciplinary optimization, Vo.21, No.2, pp.120-127, 2001   DOI
9 Aggarwal, A., Mittal, M., & Battineni, G., "Generative adversarial network: An overview of theory and applications", International Journal of Information Management Data Insights, 100004, 2021
10 Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J., "A review on generative adversarial networks: Algorithms, theory, and applications", arXiv preprint arXiv:2001.06937, 2021
11 Shen, M. H. H., & Chen, L., "A new CGAN technique for constrained topology design optimization", arXiv preprint arXiv:1901.07675, 2019
12 Rawat, S., & Shen, M. H., "A novel topology design approach using an integrated deep learning network architecture", arXiv preprint arXiv:1808.02334, 2018
13 Salehi, H., & Burgueno, R., "Emerging artificial intelligence methods in structural engineering", Engineering structures, Vol.171, pp.170~189, 2018   DOI
14 Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y., "Generative adversarial nets", Advances in neural information processing systems, Vol.27, 2014
15 Mirza, M., & Osindero, S., "Conditional generative adversarial nets", arXiv preprint arXiv:1411.1784, 2014
16 Sigmund, O., "Morphology-based black and white filters for topology optimization", Structural and Multidisciplinary Optimization, Vol.33, No.4-5, pp.401-424, 2007   DOI