An optimal quantitative two-scale expansion in stochastic homogenization of discrete elliptic equations

Abstract : We establish an optimal, linear rate of convergence for the stochastic homogenization of discrete linear elliptic equations. We consider the model problem of independent and identically distributed coefficients on a discretized unit torus. We show that the difference between the solution to the random problem on the discretized torus and the first two terms of the two-scale asymptotic expansion has the same scaling as in the periodic case. In particular the $L^2$-norm in probability of the \mbox{$H^1$-norm} in space of this error scales like $\epsilon$, where $\epsilon$ is the discretization parameter of the unit torus. The proof makes extensive use of previous results by the authors, and of recent annealed estimates on the Green's function by Marahrens and the third author.
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ESAIM: Mathematical Modelling and Numerical Analysis, EDP Sciences, 2014, 48 (2), pp.325-346. 〈10.1051/m2an/2013110〉
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Contributeur : Antoine Gloria <>
Soumis le : mercredi 3 septembre 2014 - 11:11:49
Dernière modification le : jeudi 11 janvier 2018 - 06:25:39
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Antoine Gloria, Stefan Neukamm, Felix Otto. An optimal quantitative two-scale expansion in stochastic homogenization of discrete elliptic equations. ESAIM: Mathematical Modelling and Numerical Analysis, EDP Sciences, 2014, 48 (2), pp.325-346. 〈10.1051/m2an/2013110〉. 〈hal-00863488v2〉

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