Mechanical Engineering Commons^™

Efficient Sampling For Non-Intrusive Polynomial Chaos Applications With Multiple Uncertain Input Variables, Serhat Hosder, Robert W. Walters, Michael Balch

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The accuracy and the computational efficiency of a Point-Collocation Non-Intrusive Polynomial Chaos (NIPC) method applied to stochastic problems with multiple uncertain input variables has been investigated. Two stochastic model problems with multiple uniform random variables were studied to determine the effect of different sampling methods (Random, Latin Hypercube, and Hammersley) for the selection of the collocation points. The effect of the number of collocation points on the accuracy of polynomial chaos expansions were also investigated. The results of the stochastic model problems show that all three sampling methods exhibit a similar performance in terms of the the accuracy and the …