Efficient methods for the estimation of homogenized coefficients
Provides efficient computational methods for homogenization problems, benefiting researchers in multiscale modeling and applied mathematics.
The paper introduces new methods for computing effective coefficients in homogenization of divergence-form operators with random coefficients, achieving optimal computational complexity with small constant prefactors as shown numerically.
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.