On the convergence of adaptive gPC for non-linear random difference equations: Theoretical analysis and some practical recommendations

DOI10.22436/jnsa.011.09.06zbMath1438.65316OpenAlexW2809287930WikidataQ129627197 ScholiaQ129627197MaRDI QIDQ5225445

J. Calatayud, M. Jornet, Juan-Carlos Cortés

Publication date: 22 July 2019

Published in: Journal of Nonlinear Sciences and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.22436/jnsa.011.09.06

zbMATH Keywords

numerical analysisuncertainty quantificationstochastic Galerkin projection techniquenonlinear random difference equationsadaptive gPC

Mathematics Subject Classification ID

Stochastic systems in control theory (general) (93E03) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Stochastic difference equations (39A50) Numerical methods for difference equations (65Q10)

Related Items (6)

Computing the density function of complex models with randomness by using polynomial expansions and the RVT technique. Application to the SIR epidemic model ⋮ Computational uncertainty quantification for random non-autonomous second order linear differential equations via adapted gPC: a comparative case study with random Fröbenius method and Monte Carlo simulation ⋮ Improving the approximation of the first- and second-order statistics of the response stochastic process to the random Legendre differential equation ⋮ Uncertainty quantification for nonlinear difference equations with dependent random inputs via a stochastic Galerkin projection technique ⋮ Combining polynomial chaos expansions and the random variable transformation technique to approximate the density function of stochastic problems, including some epidemiological models ⋮ Is It Worthwhile Considering Orthogonality in Generalised Polynomial Chaos Expansions Applied to Solving Stochastic Models?

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