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Record W4413224782 · doi:10.4153/s0008414x25101302

Well-posedness of Naiver–Stokes equations in logarithmic <i>Q</i> spaces covering $BMO^{-1}$ and its fractional counterpart

2025· article· en· W4413224782 on OpenAlexaff
Pengtao Li, Zengjian Lou, Zhichun Zhai

Bibliographic record

VenueCanadian Journal of Mathematics · 2025
Typearticle
Languageen
FieldMathematics
TopicNavier-Stokes equation solutions
Canadian institutionsMacEwan University
FundersNational Natural Science Foundation of China
KeywordsMathematicsLogarithmMathematical analysisMathematical physicsNavier–Stokes equationsPure mathematicsPhysicsCompressibilityMechanics

Abstract

fetched live from OpenAlex

Abstract In this article, we introduce a new logarithmic Q -type space $Q_{\ln ,\lambda }^{p,l,k}(\mathbb R^{n})$ to study the well-posedness of the classical/fractional Naiver–Stokes equations. We show that $\nabla \cdot (Q_{\ln ,\lambda }^{p,l,k}(\mathbb R^{n}))^{n}$ covers the well-known critical spaces $BMO^{-1}(\mathbb R^{n}), Q_{\alpha }^{-1}(\mathbb R^{n})$ and $\mathcal {Q}_{0}^{-1}(\mathbb R^{n})$ for the classical Naiver–Stokes equations. Moreover, it covers the fractional counterparts $BMO^{-(2\beta -1)}(\mathbb R^{n}), Q_{\alpha }^{\beta ,-1}(\mathbb R^{n})$ and even the largest critical space $\dot {B}^{-(2\beta -1)}_{\infty ,\infty }(\mathbb R^{n}).$ In doing so, we first establish some basic properties of $Q_{\ln ,\lambda }^{p,l,k}(\mathbb {R}^{n}).$ Then, via the fractional heat semigroups, we characterize the extension of $Q_{\ln ,\lambda }^{p,l,k}(\mathbb R^{n})$ to $\mathscr H_{K_{\ln }^{(l,k)}}^{p,\lambda }(\mathbb R_+^{n+1})$ which is a function space related to the weight function $K_{\ln }^{(l,k)}(\cdot )$ . This extension provides a semigroup characterization of $Q_{\ln ,\lambda }^{p,l,k}(\mathbb R^{n})$ . With this in hand, we establish the well-posedness of mild solutions to fractional Naiver–Stokes equations and fractional magneto-hydrodynamic equations, respectively, with small data in $\nabla \cdot \left (Q_{\ln ,\frac {4(1-\beta )}{n}}^{2,k,l+2(1-\beta )}(\mathbb {R}^{n})\right )^{n}$ for $k\in \mathbb {N}$ and $l&gt;n+2\beta -4.$

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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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.044
GPT teacher head0.312
Teacher spread0.268 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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