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Record W2151904273 · doi:10.1109/secon.1998.673295

Explicit and implicit time domain finite element methods using Whiteny forms

2002· article· en· W2151904273 on OpenAlexaff
M.A. Kolbehdari, Matthew Sadiku

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsCarleton University
Fundersnot available
KeywordsFinite element methodDiscretizationTime domainExtended finite element methodMixed finite element methodComputational electromagneticsSmoothed finite element methodComputer scienceApplied mathematicsDomain (mathematical analysis)Differential equationMathematicsMathematical analysisElectromagnetic fieldBoundary knot methodPhysicsBoundary element method

Abstract

fetched live from OpenAlex

This paper describes the time domain finite element methods based on p-forms of Whiteny (1957) edge and facet elements. The finite element methods are used in modeling homogeneous and non-homogeneous structures arising in computational electromagnetic applications. The time-harmonic electromagnetic fields differential equations are discretized in the spatial domain by the p-forms of Whiteny elements and in the time domain by the finite difference schemes. The finite element time domain equations are reduced to explicit and implicit differential equations which are solved using the leapfrog and Newmark schemes. The accuracy and computational efficiency of the algorithms are discussed and illustrative example is presented for an EM problem.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.027
GPT teacher head0.305
Teacher spread0.278 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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