Journal of Korean Association for Spatial Structures (한국공간구조학회논문집)

Korean Association for Spatial Structures (한국공간구조학회)
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GAN-based Data Augmentation methods for Topology Optimization

위상 최적화를 위한 생산적 적대 신경망 기반 데이터 증강 기법

  • 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)
  • 이승혜 (세종대학교 건축공학과) ;
  • 이유진 (세종대학교 건축공학과) ;
  • 이기학 (세종대학교 건축공학과) ;
  • 이재홍 (세종대학교 건축공학과)
  • Received : 2021.08.25
  • Accepted : 2021.09.16
  • Published : 2021.12.15
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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

Acknowledgement

이 논문은 행정안전부장관의 지진방재내진분야 전문인력 양성사업으로 지원되었습니다.

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