Goal-oriented error estimation for the reduced basis method, with application to sensitivity analysis

Alexandre Janon 1, 2 Maëlle Nodet 1, 3 Clémentine Prieur 1, 3
3 AIRSEA - Mathematics and computing applied to oceanic and atmospheric flows
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, UJF - Université Joseph Fourier - Grenoble 1, INPG - Institut National Polytechnique de Grenoble
Abstract : The reduced basis method is a powerful model reduction technique designed to speed up the computation of multiple numerical solutions of parametrized partial differential equations. We consider a quantity of interest, which is a linear functional of the PDE solution. A new probabilistic error bound for the reduced model is proposed. It is efficiently and explicitly computable, and we show on different examples that this error bound is sharper than existing ones. We include application of our work to sensitivity analysis studies.
Type de document :
Article dans une revue
Journal of Scientific Computing, Springer Verlag, 2016, 68 (1), pp.21-41. 〈10.1007/s10915-015-0127-z〉
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Contributeur : Alexandre Janon <>
Soumis le : jeudi 10 juillet 2014 - 19:13:01
Dernière modification le : dimanche 5 mars 2017 - 15:11:04
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Alexandre Janon, Maëlle Nodet, Clémentine Prieur. Goal-oriented error estimation for the reduced basis method, with application to sensitivity analysis. Journal of Scientific Computing, Springer Verlag, 2016, 68 (1), pp.21-41. 〈10.1007/s10915-015-0127-z〉. 〈hal-00721616v3〉

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