2016
DOI: 10.1016/j.jcp.2016.03.057
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A Lanczos model-order reduction technique to efficiently simulate electromagnetic wave propagation in dispersive media

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Cited by 21 publications
(26 citation statements)
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“…A general approach consists in transforming the nonlinear eigenvalue problem into a linear one by introducing auxiliary fields to account for material dispersion. Several variants exist and, for the sake of simplicity, we limit ourselves to a generic presentation of auxiliary‐field techniques . The latter have been initially used in the time‐domain for modeling wave propagation in dispersive media and then in the frequency domain for computing band diagrams of dispersive photonic crystals …”
Section: Overview Of Qnm Solversmentioning
confidence: 99%
See 2 more Smart Citations
“…A general approach consists in transforming the nonlinear eigenvalue problem into a linear one by introducing auxiliary fields to account for material dispersion. Several variants exist and, for the sake of simplicity, we limit ourselves to a generic presentation of auxiliary‐field techniques . The latter have been initially used in the time‐domain for modeling wave propagation in dispersive media and then in the frequency domain for computing band diagrams of dispersive photonic crystals …”
Section: Overview Of Qnm Solversmentioning
confidence: 99%
“…Note that the Drude model, a particular case for which ω0=0, requires a single auxiliary‐field, bold-italictrueJm. QNM eigensolvers based on auxiliary‐field method have been initially implemented with finite‐difference methods . The latter may introduce inaccuracies for complex geometries, which may lead to the prediction of spurious modes when discretizing curved metallic surfaces on a rectangular grid for instance.…”
Section: Overview Of Qnm Solversmentioning
confidence: 99%
See 1 more Smart Citation
“…(8) is not feasible, however, since the order of the system matrix A is simply too large. Fortunately, it can be shown that the system matrix satisfies the symmetry relation hAx; yi ¼ hx; Ayi for all x; y 2 C n ; ð11Þ where hÁ; Ái is a bilinear form given by hx; yi ¼ y T WMx with WM complex-symmetric and W a step size matrix containing the step sizes of the computational grid (see [7]). This property allows us to carry out a Lanczos-type reduction algorithm [7,8] …”
Section: Krylov Model-order Reductionmentioning
confidence: 99%
“…MOR method is useful for accelerating simulations in many fields of science and engineering [16,17,18,19,20,21,22]. In particular, MOR method is also widely used in the context of electromagnetics [19,23,24,25,26,27,28,29,30,31,32]. The overall goal of MOR can be stated as to reduce the computational requirements while maintaining an acceptable level of accuracy.…”
Section: Introductionmentioning
confidence: 99%