2020
DOI: 10.1016/j.jcp.2019.109220
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Computation of scattering resonances in absorptive and dispersive media with applications to metal-dielectric nano-structures

Abstract: In this paper we consider scattering resonance computations in optics when the resonators consist of frequency dependent and lossy materials, such as metals at optical frequencies. The proposed computational approach combines a novel hp-FEM strategy, based on dispersion analysis for complex frequencies, with a fast implementation of the nonlinear eigenvalue solver NLEIGS. Numerical computations illustrate that the pre-asymptotic phase is significantly reduced compared to standard uniform h and p strategies. Mo… Show more

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Cited by 10 publications
(15 citation statements)
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References 36 publications
(87 reference statements)
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“…This, from the accuracy of the solutions and performance of the solver. 18,19 These conclusions can also be drawn for the scattering problems treated in this work. We emphasize this point in Section 3.5.1 where formula (62) gives a pre asymptotic estimate for controlling the pollution error.…”
Section: Overviewsupporting
confidence: 54%
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“…This, from the accuracy of the solutions and performance of the solver. 18,19 These conclusions can also be drawn for the scattering problems treated in this work. We emphasize this point in Section 3.5.1 where formula (62) gives a pre asymptotic estimate for controlling the pollution error.…”
Section: Overviewsupporting
confidence: 54%
“…So, if r 1 < R( n + p n , ) < r 2 ∀ we use n = 1 as initial guess, else we set the initial n to the largest constant c ∈ (0, 1) such that r 1 < R( n + cp n , ) < r 2 . In our setting the feasibility check is computationally inexpensive compared to solving the governing equation (19). For the Armijo condition (32) we use the recommended 34 value c 1 = 10 −4 .…”
Section: Numerical Optimization Strategy (Bfgs)mentioning
confidence: 99%
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“…We also use SLEPc, the scalable library for eigenvalue problem computations [49], for performing the eigenanalysis, allowing us to apply the shift-and-invert spectral transformation. SLEPc, used to solve different eigenproblems (see, e.g., [44,[65][66][67][68][69]), employs the Krylov-Schur algorithm by default, which is equal to the thick-restart Lanczos algorithm in the case of generalized Hermitian eigenproblems. SLEPc calculates almost the same number of eigenpairs for each shift, allowing us to estimate the computational costs efficiently.…”
Section: Implementation Detailsmentioning
confidence: 99%