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Record W4413469106 · doi:10.1103/z6zc-hz1f

Nonlocal far-field modeling of multichannel metastructures

2025· article· en· W4413469106 on OpenAlexaff
Peyman Abdipour, George V. Eleftheriades

Bibliographic record

VenuePhysical review. B./Physical review. B · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFloquet theoryScatteringPosition (finance)PhysicsField (mathematics)MetamaterialMicrowaveNear and far fieldOpticsComputational physicsQuantum mechanicsMathematicsNonlinear system

Abstract

fetched live from OpenAlex

Periodic metastructures (including those with a macroperiodicity) have found numerous applications within the microwave to the optical regime. The analysis and synthesis of these structures generally requires the retrieval of equivalent material parameters (EMPs). To achieve this, the well-known approach is the locally periodic approximation (LPA), which is not always valid, e.g., when the angular dependence of the scattering behavior is pronounced. Here, through semianalytical closed-form expressions, we present an alternative approach to nonlocally model metastructures supporting multiple propagating Floquet-Bloch (FB) modes. This approach can be utilized to extract spatially dispersive (nonlocal) and/or position-dependent EMPs from the scattering parameters (S-parameters). The proposed framework can determine heterogeneous angle-dependent EMPs, replicating the scattering behavior of multichannel metastructures, and it provides an intermediary tool for the design of metastructures with macroperiodicity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.393
Teacher spread0.369 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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