  Typical engineering design tasks require the effort to modify designs
iteratively until they meet certain constraints, i.e., performance or attribute
requirements. Past work has proposed ways to solve the inverse design problem,
where desired designs are directly generated from specified requirements, thus
avoid the trial and error process. Among those approaches, the conditional deep
generative model shows great potential since 1) it works for complex
high-dimensional designs and 2) it can generate multiple alternative designs
given any condition. In this work, we propose a conditional deep generative
model, Range-GAN, to achieve automatic design synthesis subject to range
constraints. The proposed model addresses the sparse conditioning issue in
data-driven inverse design problems by introducing a label-aware
self-augmentation approach. We also propose a new uniformity loss to ensure
generated designs evenly cover the given requirement range. Through a
real-world example of constrained 3D shape generation, we show that the
label-aware self-augmentation leads to an average improvement of 14% on the
constraint satisfaction for generated 3D shapes, and the uniformity loss leads
to a 125% average increase on the uniformity of generated shapes' attributes.
This work laid the foundation for data-driven inverse design problems where we
consider range constraints and there are sparse regions in the condition space.
