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.

Abstract
1. 서론
2. 생산적 적대 신경망
    2.1 GAN
    2.2 WGAN
    2.3 DCGAN
    2.4 LSGAN
    2.5 CGAN
    2.6 매개변수
3. 위상 최적화 기법
4. 수치해석
    4.1 MBB
    4.2 데이터 생성
    4.2 생성 결과 비교
5. 결론
References

• 이승혜(세종대학교 건축공학과) | Lee Seunghye (Dept. of Architectural Engineering, Sejong University)
• 이유진(세종대학교 건축공학과) | Lee Yujin (Dept. of Architectural Engineering, Sejong University)
• 이기학(세종대학교 건축공학과) | Lee Kihak (Dept. of Architectural Engineering, Sejong University)
• 이재홍(세종대학교 건축공학과) | Lee Jaehong (Dept. of Architectural Engineering, Sejong University) 교신저자