Nonintrusive Polynomial Chaos Expansions for Sensitivity Analysis in Stochastic Differential Equations
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AbstractA Galerkin polynomial chaos (PC) method was recently proposed to perform variance decomposition and sensitivity analysis in stochastic differential equations (SDEs), driven by Wiener noise and involving uncertain parameters. The present paper extends the PC method to nonintrusive approaches enabling its application to more complex systems hardly amenable to stochastic Galerkin projection methods. We also discuss parallel implementations and the variance decomposition of the derived quantity of interest within the framework of nonintrusive approaches. In particular, a novel hybrid PC-sampling-based strategy is proposed in the case of nonsmooth quantities of interest (QoIs) but smooth SDE solution. Numerical examples are provided that illustrate the decomposition of the variance of QoIs into contributions arising from the uncertain parameters, the inherent stochastic forcing, and joint effects. The simulations are also used to support a brief analysis of the computational complexity of the method, providing insight on the types of problems that would benefit from the present developments.
CitationJimenez MN, Le Maître OP, Knio OM (2017) Nonintrusive Polynomial Chaos Expansions for Sensitivity Analysis in Stochastic Differential Equations. SIAM/ASA Journal on Uncertainty Quantification 5: 378–402. Available: http://dx.doi.org/10.1137/16m1061989.
SponsorsThis research was supported by the SRI UQ center of the King Abdullah University of Science and Technology.