论文标题
朝目标，可行性和以多样性为导向的深层生成模型
Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design
论文作者
论文摘要
由于其学习能力和模仿复杂的数据分布的能力，深层生成机器学习模型（DGM）在整个设计社区的流行一直在越来越受欢迎。常规训练DGM，以最大程度地减少分布与生成数据的分布之间的统计差异与对其训练的数据集的分布之间的分布。尽管足以生成“现实”假数据的任务，但该目标通常不足以设计综合任务。相反，设计问题通常要求遵守设计要求，例如性能目标和约束。在工程设计中推进DGM需要新的培训目标，以促进工程设计目标。在本文中，我们提出了第一个同时优化性能，可行性，多样性和目标成就的深层生成模型。我们对八个深层生成模型的八个评估指标进行了基准测试，这些方法侧重于设计性能目标的可行性，多样性和满意度。在具有挑战性的多目标自行车框架设计问题上测试了方法，并具有偏斜的不同数据类型的多模式数据。在八个指标中的六个指标中，提出的框架的表现胜过所有深层生成模型。
Deep Generative Machine Learning Models (DGMs) have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. DGMs are conventionally trained to minimize statistical divergence between the distribution over generated data and distribution over the dataset on which they are trained. While sufficient for the task of generating "realistic" fake data, this objective is typically insufficient for design synthesis tasks. Instead, design problems typically call for adherence to design requirements, such as performance targets and constraints. Advancing DGMs in engineering design requires new training objectives which promote engineering design objectives. In this paper, we present the first Deep Generative Model that simultaneously optimizes for performance, feasibility, diversity, and target achievement. We benchmark performance of the proposed method against several Deep Generative Models over eight evaluation metrics that focus on feasibility, diversity, and satisfaction of design performance targets. Methods are tested on a challenging multi-objective bicycle frame design problem with skewed, multimodal data of different datatypes. The proposed framework was found to outperform all Deep Generative Models in six of eight metrics.
