Abstract
We consider a multiscale elliptic eigenvalue problem that depends on n separable microscopic scales. Using multiscale homogenization, we derive the multiscale homogenized eigenvalue problem whose solution contains all the possible eigenvalues and eigenfunctions of the homogenized eigenvalue problem. We develop the sparse tensor product finite element (FE) method for solving this multiscale homogenized problem, thus bypassing the expensive task of forming the homogenized equation. Although the multiscale homogenized eigenvalue problem is posed in a high dimensional tensorized domain, the sparse tensor product FEs achieve a prescribed level of accuracy requiring an essentially optimal number of degrees of freedom which differs from the optimal one by only a possible logarithmic multiplying factor, given that the solution to this problem is sufficiently regular. We show that the regularity requirement is achieved under the Lipschitz condition on the multiscale coefficient. Numerical examples on two- and three-scale eigenvalue problems support the theoretical error estimates.
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The research was financially supported by the Singapore A*Star SERC grant 122-PSF-0007 and the Singapore MOE Tier 2 grant MOE2017-T2-2-144.
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Communicated by: Lothar Reichel
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Muoi, P.Q., Tan, W.C. & Hoang, V.H. Essentially optimal finite elements for multiscale elliptic eigenvalue problems. Adv Comput Math 47, 80 (2021). https://doi.org/10.1007/s10444-021-09903-5
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DOI: https://doi.org/10.1007/s10444-021-09903-5
Keywords
- Multiscale elliptic eigenvalue problems
- Multiscale convergence
- High dimensional finite elements
- Optimal complexity
- Sparse tensor product finite elements