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Record W2116995378 · doi:10.1109/aps.1989.134895

Effect of the stability factor on the accuracy of two-dimensional TD-FD simulation

2003· article· en· W2116995378 on OpenAlexaff
I.S. Kim, W.J.R. Hoefer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsStability (learning theory)Envelope (radar)Curl (programming language)ElectromagneticsBoundary value problemMathematical analysisMathematicsSafety factorFinite-difference time-domain methodDomain (mathematical analysis)PhysicsComputer scienceOpticsTelecommunications

Abstract

fetched live from OpenAlex

The effect of the stability factor on the computational accuracy of the time-domain finite-difference method using Maxwell's two curl equations is systematically tested for an initial boundary value problem in electromagnetics. A rectangular waveguide is chosen as the computational domain with matching and reflecting boundaries in two dimensions. The TE/sub 10/ mode is excited as an initial condition. A standing wave envelope is numerically formulated; s/sub max/ and S/sub min/ are sampled as in slotted-line measurements. Simulation results for different values of the stability factor are compared with the value 'unity' of the standing wave envelope in the matched condition. Generally, accuracy is not very sensitive to variations in the stability factor. But better accuracy has been observed at around SF=0.7 than at lower values of the stability factor. For both accuracy and economy reasons the authors suggest that a stability factor close to 0.7 should be chosen.>

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)0.003
metaresearch head score (Gemma)0.022
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.310
Teacher spread0.288 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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