MétaCan
Menu
Back to cohort
Record W1489148330

A RBF based iterative method for nonlinear electromagnetic problems

2008· article· en· W1489148330 on OpenAlexaff
Yong Zhang, Guangyuan Yang, K.R. Shao, L.D. Lavers

Bibliographic record

VenueInternational Conference on Electrical Machines and Systems · 2008
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNonlinear systemIterative methodRadial basis functionPartial differential equationCollocation methodCollocation (remote sensing)Newton's methodComputer scienceApplied mathematicsContinuationMathematical optimizationMathematicsAlgorithmDifferential equationMathematical analysisOrdinary differential equationArtificial intelligenceArtificial neural networkPhysics
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a novel Radial Basis Function (RBF) iterative method for nonlinear electromagnetic problems by introducing RBF collocation technique into Newton iteration. This method based firstly on transforming electromagnetic partial differential equations into high order coupled equations on many advantages of RBF collocation technique, then get numerical results from a novel self-optimized iteration technique by combining Newton iteration and continuation method. A classical numerical example analysis indicates that the proposed method, with high efficiency and accuracy, is suitable to nonlinear electromagnetic applications.

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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.041
GPT teacher head0.322
Teacher spread0.281 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueInternational Conference on Electrical Machines and SystemsSame topicElectromagnetic Simulation and Numerical MethodsFrench-language works237,207