MétaCan
Menu
Back to cohort
Record W2123769045 · doi:10.1109/intmag.2006.375919

A Subregion Expansion Method for Computational Electromagnetics

2006· article· en· W2123769045 on OpenAlexaff
D. Chen, K.R. Shao, Youguang Guo, J.D. Lavers

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFinite element methodPiecewiseElectromagneticsComputationComputational electromagneticsField (mathematics)Computer scienceComputational scienceElectromagnetic fieldAlgorithmFlexibility (engineering)Mathematical optimizationApplied mathematicsMathematicsElectronic engineeringMathematical analysisEngineeringPhysicsStructural engineering

Abstract

fetched live from OpenAlex

This paper presents a new semi-analytical method for computational electromagnetics. Today, the usual electromagnetic field simulation codes are mainly based on the finite element method (FEM). FEM uses the piecewise low order polynomials to approximate different kind of solution functions. The particular nature of a given problem is not considered, which greatly enhances the method's applicability but depresses the efficiency. In this method, the entire field domain has to be covered with fine mesh, especially in those parts where the field changes sharply, thus requiring a large number of elements (and also unknowns) to calculate the field with sufficient precision. This often makes the codes useless due to the computation cost (CPU time and memory) before some very complex problems. To overcome these limitations, quick and highly efficient techniques are pursued all along. The semi-analytical method is such a direction. The main idea of this scheme is to combine the advantages of both the high efficiency of analytical techniques and the flexibility of numerical methods.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.229
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.273
Teacher spread0.265 · 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 teacher head, 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
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicElectromagnetic Simulation and Numerical MethodsFrench-language works237,207