Goal-oriented error estimation for fast approximations of nonlinear problems

Alexandre Janon 1, 2, 3 Maëlle Nodet 4 Christophe Prieur 5 Clémentine Prieur 4
4 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
GIPSA-DA - Département Automatique
Abstract : The main result of this paper gives a numerically efficient method to bound the error that is made when approximating the output of a nonlinear problem depending on a unknown parameter (described by a probability distribution). The class of nonlinear problems under consideration includes high-dimensional nonlinear problems with a nonlinear output function. A goal-oriented probabilistic bound is computed by considering two phases. An offline phase dedicated to the computation of a reduced model during which the full nonlinear problem needs to be solved only a small number of times. The second phase is an online phase which approximates the output. This approach is applied to a toy model and to a nonlinear partial differential equation, more precisely the Burgers equation with unknown initial condition given by two probabilistic parameters. The savings in computational cost are evaluated and presented.
Type de document :
[Research Report] GIPSA-lab. 2016
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  • HAL Id : hal-01290887, version 1


Alexandre Janon, Maëlle Nodet, Christophe Prieur, Clémentine Prieur. Goal-oriented error estimation for fast approximations of nonlinear problems. [Research Report] GIPSA-lab. 2016. 〈hal-01290887〉



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