Spectral convergence of the generalized Polynomial Chaos reduced model obtained from the uncertain linear Boltzmann equation

Gaël Poëtte

Mathematics and Computers in Simulation (MATCOM), 2020, vol. 177, issue C, 24-45

Abstract: In this paper, we consider the linear Boltzmann equation subject to uncertainties in the initial conditions and matter parameters (cross-sections/opacities). In order to solve the underlying uncertain systems, we rely on moment theory and the construction of hierarchical moment models in the framework of parametric polynomial approximations. Such model is commonly called a generalized Polynomial Chaos (gPC) reduced model. In this paper, we prove the spectral convergence of the hierarchy of reduced model parametered by P (polynomial order) obtained from the uncertain linear Boltzmann equation.

Keywords: Uncertainty Quantification; Transport; Boltzmann; generalized Polynomial Chaos; Model reduction; Monte-Carlo solver (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:177:y:2020:i:c:p:24-45

DOI: 10.1016/j.matcom.2020.04.009

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