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Record W4317566497 · doi:10.1016/j.jde.2023.01.013

Quasi-periodic solutions to the incompressible Euler equations in dimensions two and higher

2023· article· en· W4317566497 on OpenAlexfundno aff
Alberto Enciso, Daniel Peralta‐Salas, Francisco Torres de Lizaur

Bibliographic record

VenueJournal of Differential Equations · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum chaos and dynamical systems
Canadian institutionsnot available
FundersAgencia Estatal de InvestigaciónEuropean Research CouncilEuropean CommissionNatural Sciences and Engineering Research Council of CanadaHORIZON EUROPE Framework ProgrammeBanco Bilbao Vizcaya ArgentariaFundación BBVA
KeywordsMathematicsTorusPeriodic boundary conditionsSolenoidal vector fieldDimension (graph theory)Mathematical analysisEuler equationsSpace (punctuation)Euler's formulaCompressibilitySemi-implicit Euler methodBoundary (topology)Backward Euler methodInteger (computer science)Boundary value problemVector fieldPure mathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

Building on the work of Crouseilles and Faou on the 2D case, we construct C∞ quasi-periodic solutions to the incompressible Euler equations with periodic boundary conditions in dimension 3 and in any even dimension. These solutions are genuinely high-dimensional, which is particularly interesting because there are extremely few examples of high-dimensional initial data for which global solutions are known to exist. These quasi-periodic solutions can be engineered so that they are dense on tori of arbitrary dimension embedded in the space of solenoidal vector fields. Furthermore, in the two-dimensional case we show that quasi-periodic solutions are dense in the phase space of the Euler equations. More precisely, for any integer N⩾1 we prove that any Lq initial stream function can be approximated in Lq (strongly when 1⩽q<∞ and weak-⁎ when q=∞) by smooth initial data whose solutions are dense on N-dimensional tori.

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 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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.291
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

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".

Quick stats

Citations14
Published2023
Admission routes1
Has abstractyes

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