Reduced basis approximation of parametric eigenvalue problems in presence of clusters and intersections
In this paper we discuss reduced order models for the approximation of parametric eigenvalue problems. In particular, we are interested in the presence of intersections or clusters of eigenvalues. The singularities originating by these phenomena make it hard a straightforward generalization of well...
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Veröffentlicht in: | Computational & applied mathematics 2024-12, Vol.43 (8), Article 443 |
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creator | Boffi, Daniele Halim, Abdul Priyadarshi, Gopal |
description | In this paper we discuss reduced order models for the approximation of parametric eigenvalue problems. In particular, we are interested in the presence of intersections or clusters of eigenvalues. The singularities originating by these phenomena make it hard a straightforward generalization of well known strategies normally used for standards PDEs. We investigate how the known results extend (or not) to higher order |
doi_str_mv | 10.1007/s40314-024-02917-x |
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subjects | Applications of Mathematics Approximation Clusters Computational Mathematics and Numerical Analysis Eigenvalues Mathematical Applications in Computer Science Mathematical Applications in the Physical Sciences Mathematics Mathematics and Statistics Reduced order models |
title | Reduced basis approximation of parametric eigenvalue problems in presence of clusters and intersections |
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