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Record W4313174437 · doi:10.23952/jnva.6.2022.5.06

Numerical analysis of a general elliptic variational-hemivariational inequality

2022· article· en· W4313174437 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Nonlinear and Variational Analysis · 2022
Typearticle
Languageen
FieldComputer Science
TopicContact Mechanics and Variational Inequalities
Canadian institutionsnot available
FundersEuropean Commission
KeywordsMathematicsUniquenessVariational inequalityFinite element methodApplied mathematicsNumerical analysisConvergence (economics)Galerkin methodMathematical analysisPhysics

Abstract

fetched live from OpenAlex

This paper is devoted to the numerical analysis of a general elliptic variational-hemivariational inequality. After a review of a solution existence and uniqueness result, we introduce a family of Galerkin methods to solve the problem. We prove the convergence of the numerical method under the minimal solution regularity condition available from the existence result and derive a Ca's inequality for error estimation of the numerical solutions. Then, we apply the results for the numerical analysis of a variational-hemivariational inequality in the study of a static problem which models the contact of an elastic body with a reactive foundation. In particular, under appropriate solution regularity conditions, we derive an optimal order error estimate for the linear finite element solution.

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)0.002
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.283
Teacher spread0.258 · 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