Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design. (arXiv:2205.03005v1 [cs.LG])

Web: http://arxiv.org/abs/2205.03005

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 …

arxiv deep deep generative models design index models