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Couplage stochastique-déterministe dans le cadre Arlequin et estimations d'erreurs en quantités d'intérêt

Cédric Zaccardi-2013-01-21-HAL (Le Centre pour la Communication Scientifique Directe)
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TL;DRAbstract

In design process, uncertainties have to be taken into account. Stochastic methods have therefore been proposed. Furthermore, in many cases, local defects affect strongly the behavior of a structure in a localized region while the rest of the structure is only slightly affected. In these cases, it is not reasonable to model the structure entirely at a fine scale, and multiscale methods are thus appealing. In this framework, we focused on the evaluation of a local specific quantity of interest when the Arlequin method is used to couple a deterministic model with a stochastic one. First, we give ingredients needed for the use of the method in this particular context. Second, to control the quality of the approximate solution obtained with such an approach, a goal-oriented method is introduced. Using residual-types estimates and adjoint-based techniques, a strategy for goal-oriented error estimation is presented for this coupling. Contributions of various error sources (modeling, space di

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In design process, uncertainties have to be taken into account. Stochastic methods have therefore been proposed. Furthermore, in many cases, local defects affect strongly the behavior of a structure in a localized region while the rest of the structure is only slightly affected. In these cases, it is not reasonable to model the structure entirely at a fine scale, and multiscale methods are thus appealing. In this framework, we focused on the evaluation of a local specific quantity of interest when the Arlequin method is used to couple a deterministic model with a stochastic one. First, we give ingredients needed for the use of the method in this particular context. Second, to control the quality of the approximate solution obtained with such an approach, a goal-oriented method is introduced. Using residual-types estimates and adjoint-based techniques, a strategy for goal-oriented error estimation is presented for this coupling. Contributions of various error sources (modeling, space di

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