Gaussian Process (GP) models are often used as mathematical approximations of
computationally expensive experiments. Provided that its kernel is suitably
chosen and that enough data is available to obtain a reasonable fit of the
simulator, a GP model can beneficially be used for tasks such as prediction,
optimization, or Monte-Carlo-based quantification of uncertainty. However, the
former conditions become unrealistic when using classical GPs as the dimension
of input increases. One popular alternative is then to turn to Generalized
Additive Models (GAMs), relying on the assumption that the simulator's response
can approximately be decomposed as a sum of univariate functions. If such an
approach has been successfully applied in approximation, it is nevertheless not
completely compatible with the GP framework and its versatile applications. The
ambition of the present work is to give an insight into the use of GPs for
additive models by integrating additivity within the kernel, and proposing a
parsimonious numerical method for data-driven parameter estimation. The first
part of this article deals with the kernels naturally associated to additive
processes and the properties of the GP models based on such kernels. The second
part is dedicated to a numerical procedure based on relaxation for additive
kernel parameter estimation. Finally, the efficiency of the proposed method is
illustrated and compared to other approaches on Sobol's g-function.