Tailoring Generative Adversarial Networks for Smooth Airfoil Design
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 18 Apr 2024.
Builds on
10 of 25 references.
References · 10
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
- 2021Deep Generative Models in Engineering Design: A Reviewcited by 105
- 2019Diversity-Sensitive Conditional Generative Adversarial Networkscited by 77
- 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
- 2020Airfoil Design Parameterization and Optimization using B'ezier Generative Adversarial Networkscited by 35
- 2021PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Designcited by 30
- 2018B'ezierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameterscited by 11
- 2021Inverse airfoil design method for generating varieties of smooth airfoils using conditional WGAN-gpcited by 7
- 2023Physics-guided training of GAN to improve accuracy in airfoil design synthesiscited by 4
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Citing papers per year: 2024: 2.
2 papers.