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Record W4389541462 · doi:10.17118/11143/20853

p-Refinement with curvilinear elements for a discontinuous Galerkinspectral element method wave equation solver

2023· article· en· W4389541462 on OpenAlexaff
Amit Nayak, David A. Kopriva, Catherine Mavriplis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCurvilinear coordinatesSolverDiscontinuous Galerkin methodSpectral element methodGalerkin methodApplied mathematicsElement (criminal law)Computer scienceFinite element methodMathematical analysisMathematicsMathematical optimizationPhysicsMixed finite element methodGeometry

Abstract

fetched live from OpenAlex

There is a need to develop high order numerical methods to advance computational fluid dynamics, especially in the automotive, energy and aerospace fields.The discontinuous Galerkin spectral element method (DGSEM) provides higher accuracy compared to finite element schemes, providing exponential convergence to smooth solutions, but at higher cost per degree of freedom.Grid adaptivity mitigates the higher cost of high order methods, where h-adaptivity splits elements and p-adaptivity changes the local polynomial order on a per element basis.Both are guided by a posteriori error estimators that evaluate the quality of the resolution within each element.

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 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 score0.305
Threshold uncertainty score0.859

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 head0.042
GPT teacher head0.299
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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