Mixed finite elements for the Gross–Pitaevskii eigenvalue problem: <i>a priori</i> error analysis and guaranteed lower energy bound

Abstract We establish an a priori error analysis for the lowest-order Raviart–Thomas finite element discretization of the nonlinear Gross-Pitaevskii eigenvalue problem. Optimal convergence rates are obtained for the primal and dual variables as well as for the eigenvalue and energy approximations. In contrast to conforming approaches, which naturally imply upper energy bounds, the proposed mixed discretization provides a guaranteed and asymptotically exact lower bound for the ground state energy. The theoretical results are illustrated by a series of numerical experiments.

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Publication Details

Journal
IMA Journal of Numerical Analysis
Published
2024-08-16
DOI
https://doi.org/10.1093/imanum/drae048
Citations
4
Primary Topic
Numerical methods for differential equations
Type
article
Field-Weighted Citation Impact
2.93

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article

Mixed finite elements for the Gross–Pitaevskii eigenvalue problem: a priori error analysis and guaranteed lower energy bound

Yizhou Liang, Daniel Peterseim, Moritz Hauck, Dietmar Gallistl
4 citations
IMA Journal of Numerical Analysis
Numerical methods for differential equations
2.93
article

Mixed finite elements for the Gross–Pitaevskii eigenvalue problem: a priori error analysis and guaranteed lower energy bound

Yizhou Liang, Daniel Peterseim, Moritz Hauck, Dietmar Gallistl
article en
4 citations

Abstract

Abstract We establish an a priori error analysis for the lowest-order Raviart–Thomas finite element discretization of the nonlinear Gross-Pitaevskii eigenvalue problem. Optimal convergence rates are obtained for the primal and dual variables as well as for the eigenvalue and energy approximations. In contrast to conforming approaches, which naturally imply upper energy bounds, the proposed mixed discretization provides a guaranteed and asymptotically exact lower bound for the ground state energy. The theoretical results are illustrated by a series of numerical experiments.

IMA Journal of Numerical AnalysisVol. 45(3)
University of Augsburg (DE), Ernst Abbe University of Applied Sciences Jena (DE), Chalmers University of Technology (SE), University of Gothenburg (SE)
Alexander von Humboldt-Stiftung, H2020 European Research Council
Affordable and clean energy
Openalex Percentile: Top 9%
Numerical methods for differential equations
2.93
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