New Regularity Criteria for the Navier-Stokes Equations in Terms of Pressure
Bibliographic record
Abstract
In this paper, we generalize the main results of [1] and [31] to Lorentz spaces, using a simple procedure. The main results are the following. Let $n\geq 3$ and let $u$ be a Leray-Hopf solution to the $n$-dimensional Navier-Stokes equations with viscosity $ν$ and divergence free initial condition $u_0\in L^2(\mathbb{R}^n)\cap L^{k}(\mathbb{R}^n)$ (where $k=k(s)$ is sufficiently large). Then there exists a constant $c>0$ such that if \begin{equation} \|p\|_{L^{r,\infty}(0,\infty;L^{s,\infty}(\mathbb{R}^n))}\frac{n}{2} \end{equation} or \begin{equation} \|\nabla p\|_{L^{r,\infty}(0,\infty;L^{s,\infty}(\mathbb{R}^n))}\frac{n}{3} \end{equation} then $u$ is smooth on $(0, \infty) \times \mathbb{R}^n$. Partial results in the case $n=3$ were obtained in [32], [33] and then recently extended to all appropriate pairs of $r,s$ in [14]. Our results present a unified proof which works for all dimensions $n\geq 3$ and the full range or admissible pairs, $(s,r)$.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".