PaDGAN: Learning to Generate High-Quality Novel Designs

PaDGAN: Learning to Generate High-Quality Novel Designs
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PaDGAN：学习生成高质量的新颖设计

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发表时间：
2021
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通讯作者：
Faez Ahmed
Faez Ahmed
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文献类型：
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作者：
W. Chen;Faez Ahmed

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深度生成模型被证明是自动设计综合和设计空间探索的有用工具。当应用于工程设计时，现有的生成模型面临三个挑战：（1）生成的设计缺乏多样性，并且不覆盖设计空间的所有区域，（2）难以显式地提高生成的设计的整体性能或质量，以及（3）现有模型通常不会生成训练数据域之外的新颖设计。在这篇文章中，我们同时解决这些挑战，提出了一个新的决定点过程为基础的损失函数的多样性和质量的概率建模。有了这个新的损失函数，我们开发了一个生成对抗网络的变体，名为“性能增强多样生成对抗网络”（PaDGAN），它可以生成新的高质量设计，并具有良好的设计空间覆盖率。通过使用三个合成的例子和一个真实世界的翼型设计的例子，我们证明了PaDGAN可以生成多样化和高质量的设计。与普通生成对抗网络相比，它生成的样本平均质量分数高出28%，多样性更大，并且没有模式崩溃问题。与通常通过在训练数据的边界内插值来生成新设计的典型生成模型不同，我们证明了PaDGAN将训练数据之外的设计空间边界扩展到高质量区域。所提出的方法是广泛适用于许多任务，包括设计空间探索，设计优化，和创造性的解决方案推荐。
Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: (1) generated designs lack diversity and do not cover all areas of the design space, (2) it is difficult to explicitly improve the overall performance or quality of generated designs, and (3) existing models generally do not generate novel designs, outside the domain of the training data. In this article, we simultaneously address these challenges by proposing a new determinantal point process-based loss function for probabilistic modeling of diversity and quality. With this new loss function, we develop a variant of the generative adversarial network, named “performance augmented diverse generative adversarial network” (PaDGAN), which can generate novel high-quality designs with good coverage of the design space. By using three synthetic examples and one real-world airfoil design example, we demonstrate that PaDGAN can generate diverse and high-quality designs. In comparison to a vanilla generative adversarial network, on average, it generates samples with a 28% higher mean quality score with larger diversity and without the mode collapse issue. Unlike typical generative models that usually generate new designs by interpolating within the boundary of training data, we show that PaDGAN expands the design space boundary outside the training data towards high-quality regions. The proposed method is broadly applicable to many tasks including design space exploration, design optimization, and creative solution recommendation.