  From an engineering perspective, a design should not only perform well in an
ideal condition, but should also resist noises. Such a design methodology,
namely robust design, has been widely implemented in the industry for product
quality control. However, classic robust design requires a lot of evaluations
for a single design target, while the results of these evaluations could not be
reused for a new target. To achieve a data-efficient robust design, we propose
Robust Inverse Design under Noise (RID-Noise), which can utilize existing noisy
data to train a conditional invertible neural network (cINN). Specifically, we
estimate the robustness of a design parameter by its predictability, measured
by the prediction error of a forward neural network. We also define a
sample-wise weight, which can be used in the maximum weighted likelihood
estimation of an inverse model based on a cINN. With the visual results from
experiments, we clearly justify how RID-Noise works by learning the
distribution and robustness from data. Further experiments on several
real-world benchmark tasks with noises confirm that our method is more
effective than other state-of-the-art inverse design methods. Code and
supplementary is publicly available at
https://github.com/ThyrixYang/rid-noise-aaai22
