Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design
This addresses the need for generative models that meet specific engineering design requirements, representing an incremental improvement for the design community.
The paper tackled the problem of deep generative models being insufficient for design synthesis tasks by introducing a model that simultaneously optimizes for performance, feasibility, diversity, and target achievement, outperforming other models in six out of eight metrics on a bicycle frame design problem.
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.