Computation methods based on the Wiener chaos expansion have been developed to study the behaviors of the aeroelastic system with randomparameters. It is proven that the discrete wavelet transformation is one ofthe most accurate and efficient numerical schemes for this uncertainty quantizationproblem. In this thesis, we propose the stochastic collocation methods(SCM), whichis a type of sampling method combining the strength of the MonteCarlo simulation and the stochastic Galerkin method. The convergence with respect to the number of the nodal points is investigated, and simulation results to aeroelastic models in the presence of uncertainty in the system parameter and due to the initial condition are reported. It is demonstrated that the accuracy of the SCM is comparable to those achieved by using the wavelet chaos expansion. However, the SCM is more straightforward, efficient and easy to implement. / Applied Mathematics

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