  Uncertainty Quantification (UQ) is a key discipline for computational
modeling of complex systems, enhancing reliability of engineering simulations.
In crashworthiness, having an accurate assessment of the behavior of the model
uncertainty allows reducing the number of prototypes and associated costs.
Carrying out UQ in this framework is especially challenging because it requires
highly expensive simulations. In this context, surrogate models (metamodels)
allow drastically reducing the computational cost of Monte Carlo process.
Different techniques to describe the metamodel are considered, Ordinary
Kriging, Polynomial Response Surfaces and a novel strategy (based on Proper
Generalized Decomposition) denoted by Separated Response Surface (SRS). A large
number of uncertain input parameters may jeopardize the efficiency of the
metamodels. Thus, previous to define a metamodel, kernel Principal Component
Analysis (kPCA) is found to be effective to simplify the model outcome
description. A benchmark crash test is used to show the efficiency of combining
metamodels with kPCA.
