  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.
