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

Abstract

Abstract We study the representative volume element (RVE) method, which is a method to approximately infer the effective behavior $$a_{\textrm{hom}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>a</mml:mi> <mml:mtext>hom</mml:mtext> </mml:msub> </mml:math> of a stationary random medium. The latter is described by a coefficient field a ( x ) generated from a given ensemble $$\langle \cdot \rangle $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>⟨</mml:mo> <mml:mo>·</mml:mo> <mml:mo>⟩</mml:mo> </mml:mrow> </mml:math> and the corresponding linear elliptic operator $$-\nabla \cdot a\nabla $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>-</mml:mo> <mml:mi>∇</mml:mi> <mml:mo>·</mml:mo> <mml:mi>a</mml:mi> <mml:mi>∇</mml:mi> </mml:mrow> </mml:math> . In line with the theory of homogenization, the method proceeds by computing $$d=3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> 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 $$\langle \cdot \rangle $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>⟨</mml:mo> <mml:mo>·</mml:mo> <mml:mo>⟩</mml:mo> </mml:mrow> </mml:math> . We make this point by investigating the bias (or systematic error), i.e., the difference between $$a_{\textrm{hom}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>a</mml:mi> <mml:mtext>hom</mml:mtext> </mml:msub> </mml:math> 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 $$O(L^{-1})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>L</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . In case of a suitable periodization of $$\langle \cdot \rangle $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>⟨</mml:mo> <mml:mo>·</mml:mo> <mml:mo>⟩</mml:mo> </mml:mrow> </mml:math> , we rigorously show that it is $$O(L^{-d})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>L</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mi>d</mml:mi> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . 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 $$\langle \cdot \rangle $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>⟨</mml:mo> <mml:mo>·</mml:mo> <mml:mo>⟩</mml:mo> </mml:mrow> </mml:math> 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 the Malliavin 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.

Topics & Concepts