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Article Dans Une Revue Mathematics and Computers in Simulation Année : 2020

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

Résumé

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 generalised 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.
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Dates et versions

hal-02090593 , version 1 (04-04-2019)
hal-02090593 , version 2 (03-04-2020)

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  • HAL Id : hal-02090593 , version 2

Citer

Gaël Poëtte. Spectral convergence of the generalized Polynomial Chaos reduced model obtained from the uncertain linear Boltzmann equation. Mathematics and Computers in Simulation, 2020. ⟨hal-02090593v2⟩

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