PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs
This addresses the problem of limited design exploration and performance in engineering domains, offering a method for improved design synthesis, though it is incremental as it builds on existing GAN frameworks.
The paper tackles the challenges of generating diverse, high-quality, and novel designs in engineering by proposing PaDGAN, a generative adversarial network with a new loss function based on Determinantal Point Processes. It demonstrates that PaDGAN generates designs with a 28% higher mean quality score and better diversity compared to a vanilla GAN, expanding the design space beyond training data.
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 paper, we simultaneously address these challenges by proposing a new Determinantal Point Processes 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" or PaDGAN, which can generate novel high-quality designs with good coverage of the design space. 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.