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Record W3129958999 · doi:10.3934/math.2021259

Computational study of the convection-diffusion equation using new cubic B-spline approximations

2021· article· en· W3129958999 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

VenueAIMS Mathematics · 2021
Typearticle
Languageen
FieldMathematics
TopicDifferential Equations and Numerical Methods
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPiecewiseConvection–diffusion equationMathematicsDiffusion equationConvergence (economics)Applied mathematicsStability (learning theory)Scheme (mathematics)Position (finance)DiffusionMaterial derivativePiecewise linear functionNumerical analysisNumerical stabilityMathematical optimizationMathematical analysisComputer sciencePhysics

Abstract

fetched live from OpenAlex

<abstract> This paper introduces an efficient numerical procedure based on cubic B-Spline (CuBS) with a new approximation for the second-order space derivative for computational treatment of the convection-diffusion equation (CDE). The time derivative is approximated using typical finite differences. The key benefit of the scheme is that the numerical solution is obtained as a smooth piecewise continuous function which empowers one to find approximate solution at any desired position in the domain. Further, the new approximation has considerably increased the accuracy of the scheme. A stability analysis is performed to assure that the errors do not magnify. Convergence analysis of the scheme is also discussed. The scheme is implemented on some test problems and the outcomes are contrasted with those of some current approximating techniques from the literature. It is concluded that the offered scheme is equitably superior and effective. </abstract>

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.383
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.177
GPT teacher head0.389
Teacher spread0.213 · 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