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Record W1921762735 · doi:10.1109/mwsym.2004.1338925

The complex envelope (CE) FDTD method and its numerical properties

2004· article· en· W1921762735 on OpenAlexaff
Changning Ma, Zhizhang Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFinite-difference time-domain methodEnvelope (radar)Numerical stabilityStability (learning theory)Dispersion (optics)ComputationFinite difference methodNumerical analysisComputer simulationTime domainMathematicsApplied mathematicsMathematical analysisComputer sciencePhysicsOpticsAlgorithmMechanicsTelecommunications

Abstract

fetched live from OpenAlex

The complex envelope (CE) finite-difference time-domain (FDTD) method has been proposed for solving electromagnetic fields of limited frequency-bands. However, it has been formulated often in terms of wave equations. In addition, its numerical properties have not been thoroughly studied. In this paper, a full-wave CE-FDTD method is presented and its numerical stability and dispersion properties are analyzed. It is found that if the carrier frequency is sufficiently high or numerical cell sizes are adequately large, the time step is no longer bounded by the stability condition; otherwise the time step is constrained by a CFL like stability condition. However, through the numerical dispersion analysis, the errors caused by large time step are not acceptable. Thus the CE-FDTD does not present much gain in computation efficiency over the conventional FDTD.

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)0.004
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.291
Teacher spread0.251 · 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
GenreMethods

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

Citations2
Published2004
Admission routes1
Has abstractyes

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