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Spatially Dispersive Electromagnetic Metasurfaces: Multipolar Modeling vs Extended GSTCs

2022· article· en· W4281393082 on OpenAlexaff
Jordan Dugan, João G. Nizer Rahmeier, T. Smy, Shulabh Gupta

Bibliographic record

Venue2022 16th European Conference on Antennas and Propagation (EuCAP) · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsSurface (topology)Dispersion (optics)DipoleElectromagnetic fieldPhysicsField (mathematics)Set (abstract data type)Surface waveComputational physicsCurrent (fluid)OpticsMathematical analysisGeometryMathematicsComputer scienceQuantum mechanicsPure mathematics

Abstract

fetched live from OpenAlex

This paper presents and compares two methods for modeling spatially dispersive metasurfaces as zero thickness sheets. In the first method, the standard Generalized Sheet Transition Conditions (GSTCs) are expanded to account for multipolar surface current densities. The extra terms in the current expansion allow for additional surface susceptibilities to be included which relate the multipolar components to the average fields and their spatial derivatives. The second method accounts for spatial dispersion by expressing the dipolar surface susceptibilities as complex rational polynomial functions of the transverse wave vector <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k_{\Vert}$</tex> . This yields a set of differential equations relating the field differences across the surface to the average fields at the surface, leading to an extended form of the standard GSTCs. The two methods are then compared, and the multipolar modeling method is shown to be a subset of the extended GSTC method for the case of a uniform surface.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.246
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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