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Record W2029230858 · doi:10.1016/j.jcam.2004.06.022

Three-dimensional anisotropic mesh adaptation for phase change problems

2004· article· en· W2029230858 on OpenAlexaff
Youssef Belhamadia, A. Fortin, Éric Chamberland

Bibliographic record

VenueJournal of Computational Physics · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFinite element methodPolygon meshAnisotropyMetric (unit)Phase (matter)Enhanced Data Rates for GSM EvolutionComputer scienceStefan problemApplied mathematicsMathematical optimizationNumerical analysisAdaptation (eye)Field (mathematics)MathematicsAlgorithmMathematical analysisGeometryBoundary (topology)PhysicsArtificial intelligenceOpticsEngineering

Abstract

fetched live from OpenAlex

A three-dimensional adaptive strategy for the finite element simulation of phase change problems is presented, discussed and validated. A semi-phase-field formulation is used for the solution of the Stefan problem. The adaptive method is based on the definition of edge length using a solution dependent metric and produces strongly anisotropic meshes. Numerical results illustrating the performance and accuracy of the proposed method are presented.

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)0.000
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.083
GPT teacher head0.334
Teacher spread0.251 · 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

Citations32
Published2004
Admission routes1
Has abstractyes

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