Methods for high-dimensional and computationally intensive models

Complex simulation codes such as the ones used in aerospace industry are often computationally expensive and involve a large number of variables. These features significantly hamper the estimation of rare event probabilities. To reduce the computational burden, an analysis of the most important variables of the problem can be performed before applying rare event estimation methods. Another way to reduce this burden is to build a surrogate model of the computationally costly simulation code and to perform the probability estimation on this metamodel. In this chapter, we first review the main techniques used in sensitivity analysis and then describe several surrogate models that are efficient in the probability estimation context.