The paper proposes an approach aimed at detecting optimal model parameter
combinations to achieve the most representative description of uncertainty
in the model performance. A classification problem is posed to find
the regions of good fitting models according to the values of a cost
function. Support Vector Machine (SVM) classification in the parameter
space is applied to decide if a forward model simulation is to be
computed for a particular generated model. SVM is particularly designed
to tackle classification problems in high-dimensional space in a
non-parametric and non-linear way. SVM decision boundaries determine
the regions that are subject to the largest uncertainty in the cost
function classification, and, therefore, provide guidelines for further
iterative exploration of the model space. The proposed approach is
illustrated by a synthetic example of fluid flow through porous media,
which features highly variable response due to the parameter values'
combination.