  The design dataset is the backbone of data-driven design. Ideally, the
dataset should be fairly distributed in both shape and property spaces to
efficiently explore the underlying relationship. However, the classical
experimental design focuses on shape diversity and thus yields biased
exploration in the property space. Recently developed methods either conduct
subset selection from a large dataset or employ assumptions with severe
limitations. In this paper, fairness- and uncertainty-aware data generation
(FairGen) is proposed to actively detect and generate missing properties
starting from a small dataset. At each iteration, its coverage module computes
the data coverage to guide the selection of the target properties. The
uncertainty module ensures that the generative model can make certain and thus
accurate shape predictions. Integrating the two modules, Bayesian optimization
determines the target properties, which are thereafter fed into the generative
model to predict the associated shapes. The new designs, whose properties are
analyzed by simulation, are added to the design dataset. An S-slot design
dataset case study was implemented to demonstrate the efficiency of FairGen in
auxetic structural design. Compared with grid and randomized sampling, FairGen
increased the coverage score at twice the speed and significantly expanded the
sampled region in the property space. As a result, the generative models
trained with FairGen-generated datasets showed consistent and significant
reductions in mean absolute errors.
