  In real case applications within the virtual prototyping process, it is not
always possible to reduce the complexity of the physical models and to obtain
numerical models which can be solved quickly. Usually, every single numerical
simulation takes hours or even days. Although the progresses in numerical
methods and high performance computing, in such cases, it is not possible to
explore various model configurations, hence efficient surrogate models are
required. Generally the available meta-model techniques show several advantages
and disadvantages depending on the investigated problem. In this paper we
present an automatic approach for the selection of the optimal suitable
meta-model for the actual problem. Together with an automatic reduction of the
variable space using advanced filter techniques an efficient approximation is
enabled also for high dimensional problems. This filter techniques enable a
reduction of the high dimensional variable space to a much smaller subspace
where meta-model-based sensitivity analyses are carried out to assess the
influence of important variables and to identify the optimal subspace with
corresponding surrogate model which enables the most accurate probabilistic
analysis. For this purpose we investigate variance-based and moment-free
sensitivity measures in combination with advanced meta-models as moving least
squares and kriging.
