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Deep generative models are proven to be a useful tool for automatic designsynthesis and design space exploration. When applied in engineering design,existing generative models face three challenges: 1) generated designs lackdiversity and do not cover all areas of the design space, 2) it is difficult toexplicitly improve the overall performance or quality of generated designs, and3) existing models generally do not generate novel designs, outside the domainof the training data. In this paper, we simultaneously address these challengesby proposing a new Determinantal Point Processes based loss function forprobabilistic modeling of diversity and quality. With this new loss function,we develop a variant of the Generative Adversarial Network, named "PerformanceAugmented Diverse Generative Adversarial Network" or PaDGAN, which can generatenovel high-quality designs with good coverage of the design space. Using threesynthetic examples and one real-world airfoil design example, we demonstratethat PaDGAN can generate diverse and high-quality designs. In comparison to avanilla Generative Adversarial Network, on average, it generates samples with a28% higher mean quality score with larger diversity and without the modecollapse issue. Unlike typical generative models that usually generate newdesigns by interpolating within the boundary of training data, we show thatPaDGAN expands the design space boundary outside the training data towardshigh-quality regions. The proposed method is broadly applicable to many tasksincluding design space exploration, design optimization, and creative solutionrecommendation.

PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs