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Record W4300663545 · doi:10.48550/arxiv.1702.07749

Well-balanced mesh-based and meshless schemes for the shallow-water\n equations

2017· preprint· W4300663545 on OpenAlexfundno aff
Alexander Bihlo, Scott MacLachlan

Bibliographic record

VenuearXiv (Cornell University) · 2017
Typepreprint
Language
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsConsistency (knowledge bases)Shallow water equationsMathematicsPolygon meshApplied mathematicsFinite differenceDerivative (finance)Finite difference methodMomentum (technical analysis)Mathematical analysisGeometry

Abstract

fetched live from OpenAlex

We formulate a general criterion for the exact preservation of the "lake at\nrest" solution in general mesh-based and meshless numerical schemes for the\nstrong form of the shallow-water equations with bottom topography. The main\nidea is a careful mimetic design for the spatial derivative operators in the\nmomentum flux equation that is paired with a compatible averaging rule for the\nwater column height arising in the bottom topography source term. We prove\nconsistency of the mimetic difference operators analytically and demonstrate\nthe well-balanced property numerically using finite difference and RBF-FD\nschemes in the one- and two-dimensional cases.\n

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)0.001
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.134
GPT teacher head0.260
Teacher spread0.126 · 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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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