Bias in the Representative Volume Element method: Periodize the Ensemble Instead of Its Realizations

We study the representative volume element (RVE) method, which is a method to approximately infer the effective behavior a(hom) of a stationary random medium. The latter is described by a coefficient field a(x) generated from a given ensemble and the corresponding linear elliptic operator -del center dot a del. In line with the theory of homogenization, the method proceeds by computing d = 3 correctors (d denoting the space dimension). To be numerically tractable, this computation has to be done on a finite domain: the so-called representative volume element, i.e., a large box with, say, periodic boundary conditions. The main message of this article is: Periodize the ensemble instead of its realizations. By this, we mean that it is better to sample from a suitably periodized ensemble than to periodically extend the restriction of a realization a(x) from the whole-space ensemble . We make this point by investigating the bias or systematic error), i.e., the difference between a(hom) and the expected value of the RVE method, in terms of its scaling w.r.t. the lateral size L of the box. In case of periodizing a(x), we heuristically argue that this error is generically O(L-1). In case of a suitable periodization of , we rigorously show that it is O(L-d). In fact, we give a characterization of the leading-order error term for both strategies and argue that even in the isotropic case it is generically non-degenerate. We carry out the rigorous analysis in the convenient setting of ensembles of Gaussian type, which allow for a straightforward periodization, passing via the (integrable) covariance function. This setting has also the advantage of making the Price theorem and theMalliavin calculus available for optimal stochastic estimates of correctors. We actually need control of second-order correctors to capture the leading-order error term. This is due to inversion symmetry when applying the two-scale expansion to the Green function. As a bonus, we present a stream-lined strategy to estimate the error in a higher-order two-scale expansion of the Green function.

Automated summary

This article studies the representative volume element (RVE) method, which approximates the effective behavior of a stationary random medium. The main message is to periodize the ensemble instead of its realizations, as it leads to smaller bias or systematic error. The leading-order error term is analyzed for both strategies, showing that periodizing the ensemble yields a smaller error scaling than periodizing the realization. The analysis is carried out in ensembles of Gaussian type, making use of the Price theorem and the Malliavin calculus for optimal stochastic estimates of correctors.