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Hybrid sampling/spectral method for solving stochastic coupled problems

Abstract : In this paper, we present a hybrid method that combines Monte Carlo sampling and spectral methods for solving stochastic coupled problems. After partitioning the stochastic coupled problem into subsidiary subproblems, the proposed hybrid method entails iterating between these subproblems in a way that enables the use of the Monte Carlo sampling method for subproblems that depend on a very large number of uncertain parameters and the use of spectral methods for subproblems that depend on only a small or moderate number of uncertain parameters. To facilitate communication between the subproblems, the proposed hybrid method shares between the subproblems a reference representation of all the solution random variables in the form of an ensemble of samples; for each subproblem solved by a spectral method, it uses a dimension-reduction technique to transform this reference representation into a subproblem-specifi c reduced-dimensional representation to facilitate a computationally efficient solution in a reduced-dimensional space. After laying out the theoretical framework, we provide an example relevant to microelectomechanical systems.
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Maarten Arnst, Christian Soize, Roger Ghanem. Hybrid sampling/spectral method for solving stochastic coupled problems. SIAM/ASA Journal on Uncertainty Quantification, ASA, American Statistical Association, 2013, 1 (1), pp.218-243. ⟨10.1137/120894403⟩. ⟨hal-00829060⟩

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