Efficient Uncertainty Quantification using sensitivity information in Least Squares SVM

This paper proposes a method to use the sensitivity information for efficient uncertainty quantification using the Least Squares-Support Vector Machine (LS-SVM) framework. The inclusion of the sensitivity information in the LS-SVM framework allows us to reduce the number of simulations. Finally, the proposed approach is verified with use of an circuit example with 25 random parameters.