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An accuracy comparison of polynomial chaos type methods for the propagation of uncertainties. (English) Zbl 1275.65004

Summary: F. Augustin et al. [Eur. J. Appl. Math. 19, No. 2, 149–190 (2008; Zbl 1148.65004)] considered the polynomial chaos expansion for the treatment of uncertainties in industrial applications. For many applications the method has been proven to be a computationally superior alternative to Monte Carlo evaluations. In the current overview we compare the accuracy of polynomial chaos type methods for the propagation of uncertainties in nonlinear problems and verify them on two examples relevant for the industry. For weakly nonlinear time-dependent models, the generalized Kalman filter equations define an efficient method, yielding good approximations if the quantities of interest are restricted to the first two moments of the solution. Secondly, stochastic collocation is discussed. The method is applied to delay differential equations and random ordinary differential equations. Finally, a generalized polynomial chaos method is discussed which is based on a subdivision of the random space. This approach is even suitable for highly nonlinear models.

MSC:

65C30 Numerical solutions to stochastic differential and integral equations
60H35 Computational methods for stochastic equations (aspects of stochastic analysis)
60H15 Stochastic partial differential equations (aspects of stochastic analysis)
60H10 Stochastic ordinary differential equations (aspects of stochastic analysis)
35R60 PDEs with randomness, stochastic partial differential equations
34F05 Ordinary differential equations and systems with randomness
65L60 Finite element, Rayleigh-Ritz, Galerkin and collocation methods for ordinary differential equations

Citations:

Zbl 1148.65004

Software:

RADAR5; RODAS

References:

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