Issue |
ESAIM: M2AN
Volume 52, Number 3, May–June 2018
|
|
---|---|---|
Page(s) | 869 - 891 | |
DOI | https://doi.org/10.1051/m2an/2018022 | |
Published online | 13 September 2018 |
Tensor-based multiscale method for diffusion problems in quasi-periodic heterogeneous media★
1
École Centrale de Nantes, GeM UMR CNRS 6183,
Nantes, France
2
École Centrale de Nantes, LMJL UMR CNRS 6629,
Nantes, France
* Corresponding author: quentin.ayoul-guilmard@centraliens-nantes.net
Received:
23
October
2017
Accepted:
27
March
2018
This paper proposes to address the issue of complexity reduction for the numerical simulation of multiscale media in a quasi-periodic setting. We consider a stationary elliptic diffusion equation defined on a domain D such that D̅ is the union of cells {D̅i}i∈I and we introduce a two-scale representation by identifying any function v(x) defined on D with a bi-variate function v(i,y), where i ∈ I relates to the index of the cell containing the point x and y ∈ Y relates to a local coordinate in a reference cell Y. We introduce a weak formulation of the problem in a broken Sobolev space V(D) using a discontinuous Galerkin framework. The problem is then interpreted as a tensor-structured equation by identifying V(D) with a tensor product space ℝI⊗ V(Y) of functions defined over the product set I × Y. Tensor numerical methods are then used in order to exploit approximability properties of quasi-periodic solutions by low-rank tensors.
Mathematics Subject Classification: 15A69 / 35B15 / 65N30
Key words: Quasi-periodicity / tensor approximation / discontinuous Galerkin / multiscale / heterogeneous diffusion
© EDP Sciences, SMAI 2018
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.