다중 스케일 위상최적화를 위한 조건부 적대적 생성 신경망을 통한 재료 표현Material Representation via Conditional Generative Adversarial Networks for Multi-scale Topology Optimization
- Other Titles
- Material Representation via Conditional Generative Adversarial Networks for Multi-scale Topology Optimization
- Authors
- 서민식; 민승재
- Issue Date
- Dec-2020
- Publisher
- 대한기계학회
- Keywords
- 멀티스케일(Multi-scale); 위상최적화(Topology optimization); 딥러닝(Deep learning); 적대적생성신경망(Generative adversarial networks)
- Citation
- 대한기계학회 2020년 학술대회, pp.162 - 165
- Indexed
- OTHER
- Journal Title
- 대한기계학회 2020년 학술대회
- Start Page
- 162
- End Page
- 165
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4404
- Abstract
- In this paper, a novel material representation method for multi-scale topology optimization is proposed. The number of design variables of every microstructure reduces by the generator network. The generator is trained together with the discriminator simultaneously in an adversarial way. Some of the condensed design variables are applied as conditions of the generative networks to control the microstructure much easier than without any condition. These conditions also make the generated samples be uniformly distributed without augmentation of the training data. The isotropic microstructure is tested, and the result shows the effectiveness of the proposed method. By this method, geometric constraints are not necessary in the optimization phase.
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