  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.
