Efficient Methods for the Estimation of Homogenized Coefficients

J. C. Mourrat

Research output: Contribution to journalArticlepeer-review


The main goal of this paper is to define and study new methods for the computation of effective coefficients in the homogenization of divergence-form operators with random coefficients. The methods introduced here are proved to have optimal computational complexity and are shown numerically to display small constant prefactors. In the spirit of multiscale methods, the main idea is to rely on a progressive coarsening of the problem, which we implement via a generalization of the Green–Kubo formula. The technique can be applied more generally to compute the effective diffusivity of any additive functional of a Markov process. In this broader context, we also discuss the alternative possibility of using Monte Carlo sampling and show how a simple one-step extrapolation can considerably improve the performance of this alternative method.

Original languageEnglish (US)
Pages (from-to)435-483
Number of pages49
JournalFoundations of Computational Mathematics
Issue number2
StatePublished - Apr 15 2019


  • Homogenization
  • Multiscale methods

ASJC Scopus subject areas

  • Analysis
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


Dive into the research topics of 'Efficient Methods for the Estimation of Homogenized Coefficients'. Together they form a unique fingerprint.

Cite this