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Galerkin Finite Element Method by Using Bivariate Splines for Parabolic PDEs

2013· article· en· W1831260691 on OpenAlexvenueno aff
Kai Qu, Bo Jiang

Bibliographic record

VenueProgress in applied mathematics · 2013
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsnot available
Fundersnot available
KeywordsMathematicsFinite element methodGalerkin methodBivariate analysisSpline (mechanical)Partial differential equationQuadratic equationMathematical analysisBoundary value problemParabolic partial differential equationApplied mathematicsThin plate splineSubspace topologyBoundary (topology)Discontinuous Galerkin methodSpline interpolationGeometryPhysics

Abstract

fetched live from OpenAlex

A Galerkin finite element method by using bivariate splines (GB method) is proposed for solving parabolic partial differential equations (PPDEs). Bivariate spline proper subspace of $S_4^{2,3}(\Delta_{mn}^{(2)})$ satisfying homogeneous boundary conditions on type-2 triangulations and quadratic B-spline interpolating boundary functions are primarily constructed. PPDEs are solved by the GB method.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.322
Teacher spread0.290 · 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.

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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