Constructing adaptive generalized polynomial chaos method to measure the uncertainty in continuous models: a computational approach

DOI10.1016/j.matcom.2014.09.002OpenAlexW2055880321MaRDI QIDQ2228590

J. A. Licea, Benito M. Chen-Charpentier, Juan-Carlos Cortés, M. D. Roselló, Francisco-José Santonja, José Vicente Romero, Rafael-Jacinto Villanueva

Publication date: 19 February 2021

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10251/63714

zbMATH Keywords

random differential equationscomputingadaptive polynomial chaos

Mathematics Subject Classification ID

Numerical analysis (65-XX) Computer science (68-XX)

Related Items (7)

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 ⋮ Uncertainty quantification for nonlinear difference equations with dependent random inputs via a stochastic Galerkin projection technique ⋮ Improving adaptive generalized polynomial chaos method to solve nonlinear random differential equations by the random variable transformation technique ⋮ On the convergence of adaptive gPC for non-linear random difference equations: Theoretical analysis and some practical recommendations ⋮ 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?

Uses Software

• KernSmooth

Cites Work

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