  Complex computer codes are often too time expensive to be directly used to
perform uncertainty, sensitivity, optimization and robustness analyses. A
widely accepted method to circumvent this problem consists in replacing
cpu-time expensive computer models by cpu inexpensive mathematical functions,
called metamodels. For example, the Gaussian process (Gp) model has shown
strong capabilities to solve practical problems , often involving several
interlinked issues. However, in case of high dimensional experiments (with
typically several tens of inputs), the Gp metamodel building process remains
difficult, even unfeasible, and application of variable selection techniques
cannot be avoided. In this paper, we present a general methodology allowing to
build a Gp metamodel with large number of inputs in a very efficient manner.
While our work focused on the Gp metamodel, its principles are fully generic
and can be applied to any types of metamodel. The objective is twofold:
estimating from a minimal number of computer experiments a highly predictive
metamodel. This methodology is successfully applied on an industrial computer
code.
