Flux norm approach to finite dimensional homogenization approximations with non-separated scales and high contrast
Leonid Berlyand, Houman Owhadi

TL;DR
This paper introduces a flux norm approach for finite-dimensional homogenization of elliptic and elasticity equations with rough, high-contrast coefficients, enabling error estimates independent of regularity and contrast.
Contribution
It establishes a transfer property in the flux norm linking rough coefficient solutions to smooth ones, facilitating optimal approximation spaces for complex media.
Findings
Error bounds independent of coefficient contrast
Construction of optimal approximation spaces
Extension to elasticity equations
Abstract
We consider divergence-form scalar elliptic equations and vectorial equations for elasticity with rough (, ) coefficients that, in particular, model media with non-separated scales and high contrast in material properties. We define the flux norm as the norm of the potential part of the fluxes of solutions, which is equivalent to the usual -norm. We show that in the flux norm, the error associated with approximating, in a properly defined finite-dimensional space, the set of solutions of the aforementioned PDEs with rough coefficients is equal to the error associated with approximating the set of solutions of the same type of PDEs with smooth coefficients in a standard space (e.g., piecewise polynomial). We refer to this property as the {\it transfer property}. A simple application of this property is the construction of finite…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
