  Performing a computer experiment can be viewed as observing a mapping between
the model parameters and the corresponding model outputs predicted by the
computer model. In view of this, experimental design for computer experiments
can be thought of as devising a reliable procedure for finding configurations
of design points in the parameter space so that their images represent the
manifold parametrized by such a mapping (i.e., computer experiments).
Traditional space-filling designs aim to achieve this goal by filling the
parameter space with design points that are as "uniform" as possible in the
parameter space. However, the resulting design points may be non-uniform in the
model output space and hence fail to provide a reliable representation of the
manifold, becoming highly inefficient or even misleading in case the computer
experiments are non-linear. In this paper, we propose an iterative algorithm
that fills in the model output manifold uniformly---rather than the parameter
space uniformly---so that one could obtain a reliable understanding of the
model behaviors with the minimal number of design points.
