  Metamodeling of complex numerical systems has recently attracted the interest
of the mathematical programming community. Despite the progress in high
performance computing, simulations remain costly, as a matter of fact, the
assessment of the exposure to radio frequency electromagnetic fields is
computationally prohibitive since one simulation can require hours. Moreover,
in many engineering problems, carrying out deterministic numerical operations
without considering uncertainties can lead to unreliable designs. In this paper
we focus on the surrogate modeling of a particular type of computational models
called stochastic simulators. In contrast to deterministic simulators which
yield a unique output for each set of input parameters, stochastic simulators
inherently contain some sources of randomness and the output at a given point
is a probability density function. Characterizing the stochastic simulators is
even more time consuming. This paper represents stochastic simulators as a
stochastic process and describes a metamodeling approach based on the
Karhunen-Lo\`eve spectral decomposition.
