Tailoring Generative Adversarial Networks for Smooth Airfoil Design
<|reference_start|>Tailoring Generative Adversarial Networks for Smooth Airfoil Design: 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.<|reference_end|>
@article{chattoraj2024tailoring,
    title={Tailoring Generative Adversarial Networks for Smooth Airfoil Design},
    author={Joyjit Chattoraj, Jian Cheng Wong, Zhang Zexuan, Manna Dai, Xia
  Yingzhi, Li Jichao, Xu Xinxing, Ooi Chin Chun, Yang Feng, Dao My Ha, Liu Yong},
    journal={arXiv preprint arXiv:2404.11816},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11816},
    primaryClass={cs.LG}
}