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Record W1891577001 · doi:10.1016/j.proeng.2015.10.129

Moving Mesh Methods on Parametric Surfaces

2015· article· en· W1891577001 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProcedia Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsParametric statisticsComputer scienceParametric surfaceMaterials scienceGeometryStructural engineeringComputer graphics (images)GeologyEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Many phenomena in the applied and natural sciences occur on surfaces. To solve accurately the corresponding partial differential equations (PDEs), it is often necessary to adapt the mesh, based upon the geometry of the surface, or based upon the behaviour of the PDE solution. Moving mesh methods are particularly efficient strategies in many situations. PDEs explicitly involving the mesh speed, called moving mesh PDEs (MMPDEs), offer a robust technique to adapt the mesh. In this work, we implement, with the C++ finite element library deal.II, a mesh adaptation based on Winslow's adaptation functional. We generalize the moving mesh problem to curved surfaces by deriving appropriate mathematical and finite element formulations. Furthermore, a simple method using surface parameterization is developed and implemented using deal.II. The results, for both fixed and dynamically adapting meshes, demonstrate the effectiveness of the 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.

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)0.003
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 score0.163
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 head0.053
GPT teacher head0.343
Teacher spread0.289 · 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