论文标题
Mo-Padgan：通过增强多元性能生成多种设计
MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement
论文作者
论文摘要
深层生成模型已被证明可用于自动设计合成和设计空间探索。但是，当应用于工程设计时，他们面临三个挑战：1）生成的设计缺乏多样性，2）很难明确改善生成设计的所有性能指标，而3）现有模型通常不会产生高性能的新颖设计，在培训数据的领域之外。为了应对这些挑战，我们提出了Mo-Padgan，其中包含基于确定点过程的新的损失函数，用于多样性和性能的概率建模。通过现实世界中的翼型设计示例，我们证明了Mo-Padgan将设计空间的现有边界扩展到高性能区域，并生成具有高度多样性和超过培训数据的新设计。
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.
