  Uncertainty Quantification (UQ) is a booming discipline for complex
computational models based on the analysis of robustness, reliability and
credibility. UQ analysis for nonlinear crash models with high dimensional
outputs presents important challenges. In crashworthiness, nonlinear structural
behaviours with multiple hidden modes require expensive models (18 hours for a
single run). Surrogate models (metamodels) allow substituting the full order
model, introducing a response surface for a reduced training set of numerical
experiments. Moreover, uncertain input and large number of degrees of freedom
result in high dimensional problems, which derives to a bottle neck that blocks
the computational efficiency of the metamodels. Kernel Principal Component
Analysis (kPCA) is a multidimensionality reduction technique for non-linear
problems, with the advantage of capturing the most relevant information from
the response and improving the efficiency of the metamodel. Aiming to compute
the minimum number of samples with the full order model. The proposed
methodology is tested with a practical industrial problem that arises from the
automotive industry.
