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Record W2804030256 · doi:10.1137/17m113544x

Analysis of Galerkin FEMs for Mixed Formulation of Time-Dependent Ginzburg--Landau Equations Under Temporal Gauge

2018· article· en· W2804030256 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

VenueSIAM Journal on Numerical Analysis · 2018
Typearticle
Languageen
FieldMathematics
TopicNumerical methods for differential equations
Canadian institutionsToronto Metropolitan University
FundersResearch Grants Council, University Grants CommitteeCity University of Hong Kong
KeywordsMathematicsDiscontinuous Galerkin methodMagnetic fieldVortexDegeneracy (biology)SuperconductivityGalerkin methodLandau quantizationGauge theoryNumerical analysisNorm (philosophy)Gauge (firearms)Mathematical analysisApplied mathematicsPhysicsMathematical physicsFinite element methodQuantum mechanicsLawMechanics

Abstract

fetched live from OpenAlex

The paper focuses on analysis of linearized Galerkin FEMs for a mixed formulation of the time-dependent Ginzburg--Landau equations under the temporal gauge. We provide optimal error estimates in $L^2$-norm for the order parameter $\psi_h$ and the magnetic field $\sigma_h$ unconditionally, although the accuracy of the numerical magnetic potential $\mathbf{A}_h$ is one-order lower than the optimal one due to the degeneracy of the magnetic potential equation. Since the states of superconductors are determined by the order parameter $\psi_h$ (or the density of the superconducting electron pairs $|\psi_h|$), the accuracy of $\psi_h$ is more important for the vortex simulation in superconditors. Our analysis is based on a nonclassical Ritz projection, which may reduce the pollution of inaccuracy of the numerical magnetic potential in analysis. Numerical experiments confirm our theoretical analysis.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.379
Teacher spread0.307 · 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