Efficient classification based methods for global sensitivity analysis

New classification based methods for global sensitivity analysis of structural models are presented which do not require the full approximation of the model response for qualitatively good sensitivity measures. Instead, only the level sets of the model response are identified by partitioning it into a number of classes with a few available sample points. The average change in class memberships of simulated points on the model domain is considered as sensitivity measure. The new methods are realized using Support Vector Machines and their results are compared with existing methods by using analytical as well as practical industry examples.