MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement

• URL: http://arxiv.org/abs/2007.04790v1
• Date: Tue, 7 Jul 2020 21:57:29 GMT
• Title: MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement
• Authors: Wei Chen and Faez Ahmed
• Abstract summary: Deep generative models have proven useful for automatic design synthesis and design space exploration. They face three challenges when applied to engineering design: 1) generated designs lack diversity, 2) it is difficult to explicitly improve all the performance measures of generated designs, and 3) existing models generally do not generate high-performance novel designs. We propose MO-PaDGAN, which contains a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
• Score: 13.866787416457454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep generative models have proven useful for automatic design synthesis and design space exploration. However, they face three challenges when applied to engineering design: 1) generated designs lack diversity, 2) it is difficult to explicitly improve all the performance measures of generated designs, and 3) existing models generally do not generate high-performance novel designs, outside the domain of the training data. To address these challenges, we propose MO-PaDGAN, which contains a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances. Through a real-world airfoil design example, we demonstrate that MO-PaDGAN expands the existing boundary of the design space towards high-performance regions and generates new designs with high diversity and performances exceeding training data.

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