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Record W2022599090 · doi:10.1137/05063756x

The Optimal Convergence of the<i>h</i>‐<i>p</i>Version of the Finite Element Method with Quasi‐Uniform Meshes

2007· article· en· W2022599090 on OpenAlexafffund
Benqi Guo, Weiwei Sun

Bibliographic record

VenueSIAM Journal on Numerical Analysis · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsUniversity of Manitoba
FundersE-Institutes of Shanghai Municipal Education CommissionShanghai Normal UniversityNatural Sciences and Engineering Research Council of CanadaShanghai Municipal Education CommissionCity University of Hong Kong
KeywordsPolygon meshMathematicsFinite element methodConvergence (economics)Uniform convergenceVolume meshUpper and lower boundsApplied mathematicsElement (criminal law)Mathematical analysisGeometryMesh generationComputer science

Abstract

fetched live from OpenAlex

In the framework of the Jacobi‐weighted Besov spaces, we analyze the convergence of the h‐p version of finite element solutions on quasi‐uniform meshes and the lower and upper bounds of errors for elliptic problems on polygons. Both lower and upper bounds are proved to be optimal in h and p, which leads to the optimal convergence of the h‐p version of the finite element method with quasi‐uniform meshes for elliptic problems on polygons. The results proved for the h‐p version include the h‐version with quasi‐uniform meshes and the p‐version with quasi‐uniform degrees as two special cases.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.003
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0020.002
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 head0.011
GPT teacher head0.288
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 designTheoretical or conceptual
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

Citations33
Published2007
Admission routes2
Has abstractyes

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