Sensitivity computation for uncertain dynamical systems using high-dimensional model representation and hierarchical grids

Global sensitivity analysis is an important tool for uncertainty analysis of systems with uncertain model parameters. A general framework for the determination of sensitivity measures for fuzzy uncertainty analysis is presented. The derivation is founded on the high-dimensional model representation, which provides a common basis with Sobol indices, illustrating the similarities and di erences of fuzzy and stochastic uncertainty analysis. For the numerical calculation, a sparse-grid approach is suggested, providing an e cient realization due to the direct relationship between hierarchical grids and the sensitivity measures. c 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of Institute of Engineering and Computational Mechanics University of Stuttgart.