MétaCan
Menu
Back to cohort
Record W2252444624 · doi:10.12732/ijam.v28i6.3

ON A STABILIZED FINITE ELEMENT METHOD WITH MESH ADAPTIVE PROCEDURE FOR CONVECTION--DIFFUSION PROBLEMS

2015· article· en· W2252444624 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Apllied Mathematics · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsLaurentian UniversityUniversité de Moncton
Fundersnot available
KeywordsFinite element methodConvection–diffusion equationMethod of mean weighted residualsPolygon meshApplied mathematicsGalerkin methodEstimatorMixed finite element methodDiffusionA priori and a posterioriAdaptive mesh refinementMathematicsPartial differential equationUpwind schemeScheme (mathematics)Numerical analysisComputer scienceMathematical analysisGeometryPhysicsComputational scienceDiscretizationThermodynamics

Abstract

fetched live from OpenAlex

Computing solutions of convection-diffusion equations is an important and challenging problem from the numerical point of view. We present in this work a numerical scheme to study this problem. The scheme combines a stabilized finite element method introduced in [Serghini Mounim, A stabilized finite element method for convection-diffusion problems, Mumer. Methods Partial Differential Eq 28: 2012], with an adaptive mesh refinement procedure which is based on the residual a posteriori error estimators. It is worthwhile to point out that the numerical results indicate that the stabilization parameter introduced in [Serghini Mounim, A stabilized finite element method for convection-diffusion problems, Numer. Methods Partial Differential Eq. 28 (2012), 1916-1943] gives much better results than the standard Streamline upwind/Petrov-Galerkin (SUPG) one.

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.331
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.047
GPT teacher head0.335
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