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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.11816 |
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| _version_ | 1866914759382663168 |
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| author | Chattoraj, Joyjit Wong, Jian Cheng Zexuan, Zhang Dai, Manna Yingzhi, Xia Jichao, Li Xinxing, Xu Chun, Ooi Chin Feng, Yang Ha, Dao My Yong, Liu |
| author_facet | Chattoraj, Joyjit Wong, Jian Cheng Zexuan, Zhang Dai, Manna Yingzhi, Xia Jichao, Li Xinxing, Xu Chun, Ooi Chin Feng, Yang Ha, Dao My Yong, Liu |
| contents | In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils. Generative Adversarial Network (GAN), a widely employed generative AI technique, has proven instrumental in synthesizing airfoil designs. However, a common limitation of GAN is the inherent lack of smoothness in the generated airfoil surfaces. To address this issue, we present a GAN model featuring a customized loss function built to produce seamlessly contoured airfoil designs. Additionally, our model demonstrates a substantial increase in design diversity compared to a conventional GAN augmented with a post-processing smoothing filter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11816 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tailoring Generative Adversarial Networks for Smooth Airfoil Design Chattoraj, Joyjit Wong, Jian Cheng Zexuan, Zhang Dai, Manna Yingzhi, Xia Jichao, Li Xinxing, Xu Chun, Ooi Chin Feng, Yang Ha, Dao My Yong, Liu Machine Learning In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils. Generative Adversarial Network (GAN), a widely employed generative AI technique, has proven instrumental in synthesizing airfoil designs. However, a common limitation of GAN is the inherent lack of smoothness in the generated airfoil surfaces. To address this issue, we present a GAN model featuring a customized loss function built to produce seamlessly contoured airfoil designs. Additionally, our model demonstrates a substantial increase in design diversity compared to a conventional GAN augmented with a post-processing smoothing filter. |
| title | Tailoring Generative Adversarial Networks for Smooth Airfoil Design |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.11816 |