The purpose of this thesis is to investigate innovative response surface methods to address the problem of sensitivity analysis of complex and computationally demanding computer codes. To this end, we have focused our research work on methods based on ANOVA decomposition. We proposed to use a smoothing spline nonparametric regression method, which is an ANOVA based method that is performed using an iterative algorithm, combining an estimation procedure and a variable selection procedure. The latter can become computationally demanding when dealing with high dimensional problems. To deal with this, we developed a new iterative shrinkage algorithm, which is conceptually simple and efficient. Using the fact that this method is an ANOVA based method, it allows us to introduce a new method for computing sensitivity indices. Inspiring by this response surface method, we developed a new method to approximate the model for which the response involves more complex outputs. This method is based on a multiresolution analysis with wavelet decompositions, which is well known to produce very good approximations on highly nonlinear or discontinuous models. Finally we considered the problem of approximating the computer code when the outputs are times series. We proposed an original method for performing this task, combining the smoothing spline response surface method and wavelet decomposition. To assess the efficiency of the developed methods, numerical experiments on analytical functions and reservoir engineering test cases are presented.
Response surface methods based on analysis of variance expansion for sensitivity analysis