MO-PaDGAN：生成具有多变量性能增强的多样化设计
MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement
摘要：
深度生成模型已被证明对自动设计综合和设计空间探索非常有用。然而，当应用于工程设计时，它们面临三个挑战：1）生成的设计缺乏多样性，2）难以显式地改进生成的设计的所有性能度量，以及3）现有模型通常在训练数据的域之外不生成高性能的新颖设计。为了解决这些挑战，我们提出了MO-PaDGAN，其中包含一个新的基于损失函数的概率建模的多样性和性能的决定点过程。通过一个真实的翼型设计实例，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，并生成具有高多样性和超过训练数据的性能的新设计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
