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Record W4385884194 · doi:10.3934/dcds.2023092

Large-time behavior of solutions for unipolar Euler-Poisson equations with critical over-damping

2023· article· en· W4385884194 on OpenAlexafffund
Jianing Xu, Ming Mei, Hai-Liang Li

Bibliographic record

VenueDiscrete and Continuous Dynamical Systems · 2023
Typearticle
Languageen
FieldMathematics
TopicNavier-Stokes equation solutions
Canadian institutionsChamplain Regional CollegeMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCapital Normal University
KeywordsLogarithmEuler's formulaInteger (computer science)MathematicsMathematical analysisPoisson distributionPerturbation (astronomy)Initial value problemEuler equationsCauchy distributionConvergence (economics)Backward Euler methodApplied mathematicsPhysicsComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

This paper is concerned with the large-time behavior of solutions to the Cauchy problem for the one-dimensional unipolar Euler-Poisson equations with critical time-dependent over-damping. We prove that the Cauchy problem admits a unique global smooth solution which time-asymptotically converges to the stationary solution in the logarithmic form $ O(\ln^{-\frac{k}{2}}(1+t)) $ for the integer $ k\in[1, +\infty) $. In particular, the integer $ k $ can be large enough as the initial perturbation is small enough. This convergence rate is much better than the previous studies with critical over-damping. The proof is based on the technical time-weighted energy estimates and the mathematical induction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.044
GPT teacher head0.334
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes2
Has abstractyes

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