  Deep Generative Machine Learning Models have been growing in popularity
across the design community thanks to their ability to learn and mimic complex
data distributions. While early works are promising, further advancement will
depend on addressing several critical considerations such as design quality,
feasibility, novelty, and targeted inverse design. We propose the Design Target
Achievement Index (DTAI), a differentiable, tunable metric that scores a
design's ability to achieve designer-specified minimum performance targets. We
demonstrate that DTAI can drastically improve the performance of generated
designs when directly used as a training loss in Deep Generative Models. We
apply the DTAI loss to a Performance-Augmented Diverse GAN (PaDGAN) and
demonstrate superior generative performance compared to a set of baseline Deep
Generative Models including a Multi-Objective PaDGAN and specialized tabular
generation algorithms like the Conditional Tabular GAN (CTGAN). We further
enhance PaDGAN with an auxiliary feasibility classifier to encourage feasible
designs. To evaluate methods, we propose a comprehensive set of evaluation
metrics for generative methods that focus on feasibility, diversity, and
satisfaction of design performance targets. Methods are tested on a challenging
benchmarking problem: the FRAMED bicycle frame design dataset featuring
mixed-datatype parametric data, heavily skewed and multimodal distributions,
and ten competing performance objectives.
