This dissertation addresses the issue of dealing with uncertainties when surrogate models are used to approximate costly numerical simulators. First, we propose alternatives to compensate for the surrogate model errors in order to obtain safe predictions with minimal impact on the accuracy (conservative strategies). The efficiency of the different methods are analyzed with the help of engineering problems, and are applied to the optimization of a laminate composite under reliability constraints. We also propose two contributions to the field of design of experiments (DoE) in order to minimize the uncertainty of surrogate models. Firstly, we developed a sequential method to build DoEs that minimize the error in a target region of the design space. Secondly, we proposed optimal sampling strategies when simulators with noisy responses are considered.
