  Space-filling designs are commonly used in computer experiments to fill the
space of inputs so that the input-output relationship can be accurately
estimated. However, in certain applications such as inverse design or
feature-based modeling, the aim is to fill the response or feature space. In
this article, we propose a new experimental design framework that aims to fill
the space of the outputs (responses or features). The design is adaptive and
model-free, and therefore is expected to be robust to different kinds of
modeling choices and input-output relationships. Several examples are given to
show the advantages of the proposed method over the traditional input
space-filling designs.
