Evidence map›Paper›PMID 41163817›Full record

ArticleNonlinear dynamics2025

Computation of simple invariant solutions in fluid turbulence with the aid of deep learning.

Jacob Page

Abstract read
In one paragraph

Article in Nonlinear dynamics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Jacob PageSchool of Mathematics & Maxwell Institute for Mathematical Sciences, University of Edinburgh, EH9 3FD, Edinburgh, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The dynamical systems view of a turbulent fluid flow provides a tantalizing connection between the self-sustaining nonlinear mechanics of turbulence and its more well-known statistical properties, and promises to open up new avenues in our ability to understand, predict and control complex fluid motion. However, successful application of these ideas to a high Reynolds number (

Indexed as

Fluid dynamicsMachine learningTurbulence

Identifiers

PMID41163817
PMCPMC12559166

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.