한국공간구조학회논문집 (Journal of Korean Association for Spatial Structures)

  • 제21권4호
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  • Pages.39-48
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  • 2021
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  • 1598-4095(pISSN)
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  • 2287-7401(eISSN)
한국공간구조학회 (Korean Association for Spatial Structures)
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위상 최적화를 위한 생산적 적대 신경망 기반 데이터 증강 기법

GAN-based Data Augmentation methods for Topology Optimization

  • 이승혜 (세종대학교 건축공학과) ;
  • 이유진 (세종대학교 건축공학과) ;
  • 이기학 (세종대학교 건축공학과) ;
  • 이재홍 (세종대학교 건축공학과)
  • 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)
  • 투고 : 2021.08.25
  • 심사 : 2021.09.16
  • 발행 : 2021.12.15
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초록

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.

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과제정보

이 논문은 행정안전부장관의 지진방재내진분야 전문인력 양성사업으로 지원되었습니다.

참고문헌

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