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Record W2162637429 · doi:10.2528/pier09061708

EFFICIENT APPROACH FOR SENSITIVITY ANALYSIS OF LOSSY AND LEAKY STRUCTURES USING FDTD

2009· article· en· W2162637429 on OpenAlexaff
Mohamed A. Swillam, Ramy H. Gohary, Mohamed H. Bakr, Xun Li

Bibliographic record

VenueElectromagnetic waves · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsCommunications Research Centre CanadaMcMaster University
Fundersnot available
KeywordsFinite-difference time-domain methodLossy compressionSensitivity (control systems)Computer scienceMathematicsMaterials scienceElectronic engineeringPhysicsOpticsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

An efficient approach is utilized for extracting the modal parameters of high frequency structures and their sensitivities with respect to all the design parameters.Using one FDTD simulation, the modal parameters of all the guided and leaky modes are extracted over the frequency band of interest.An adapted version of the matrix pencil method is utilized for efficient extraction of the modal parameters.In addition, using no extra simulations, the sensitivities of the propagation constants with respect to all the design parameters of the structure are extracted regardless of their number.The computational time is a small fraction of the cost of similar approaches.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.234
Teacher spread0.222 · 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

Citations25
Published2009
Admission routes1
Has abstractyes

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