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Fast recovery of parametric eigenvalues depending on several parameters and location of high order exceptional points

Fast recovery of parametric eigenvalues depending on several parameters and location of high order exceptional points

来源:Arxiv_logoArxiv
英文摘要

A numerical algorithm is proposed to deal with parametric eigenvalue problems involving non-Hermitian matrices and is exploited to find location of defective eigenvalues in the parameter space of non-hermitian parametric eigenvalue problems. These non-Hermitian degeneracies also called exceptional points (EP) have raised considerable attention in the scientific community as these can have a great impact in a variety of physical problems. The method first requires the computation of high order derivatives of a few selected eigenvalues with respect to each parameter involved. The second step is to recombine these quantities to form new coefficients associated with a partial characteristic polynomial (PCP). By construction, these coefficients are regular functions in a large domain of the parameter space which means that the PCP allows one to recover the selected eigenvalues as well as the localization of high order EPs by simply using standard root-finding algorithms. The versatility of the proposed approach is tested on several applications, from mass-spring systems to guided acoustic waves with absorbing walls and rooms acoustics. The scalability of the method to large sparse matrices arising from conventional discretization techniques such as the finite element method is demonstrated. The proposed approach can be extended to a large number of applications where EPs play an important role in quantum mechanics, optics and photonics or in mechanical engineering.

Benoit Nennig、Martin Ghienne、Emmanuel Perrey-Debain

物理学数学

Benoit Nennig,Martin Ghienne,Emmanuel Perrey-Debain.Fast recovery of parametric eigenvalues depending on several parameters and location of high order exceptional points[EB/OL].(2025-05-09)[2025-07-25].https://arxiv.org/abs/2505.06141.点此复制

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