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Record W2094718446 · doi:10.1002/cpa.20278

Tensor products and correlation estimates with applications to nonlinear Schrödinger equations

2009· article· en· W2094718446 on OpenAlexaff
J. Colliander, Nikolaos Tzirakis, Manoussos G. Grillakis

Bibliographic record

VenueCommunications on Pure and Applied Mathematics · 2009
Typearticle
Languageen
FieldMathematics
TopicAdvanced Mathematical Physics Problems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMathematicsNonlinear systemDimension (graph theory)Bilinear interpolationTensor (intrinsic definition)Conservation lawDiagonalSobolev spaceBilinear formCommutatorClass (philosophy)Pure mathematicsSpace (punctuation)Mathematical analysisAlgebra over a fieldQuantum mechanicsGeometryPhysics

Abstract

fetched live from OpenAlex

Abstract We prove new interaction Morawetz‐type (correlation) estimates in one and two dimensions. In dimension 2 the estimate corresponds to the nonlinear diagonal analogue of Bourgain's bilinear refinement of Strichartz. For the two‐dimensional case we provide a proof in two different ways. First, we follow the original approach of Lin and Strauss but applied to tensor products of solutions. We then demonstrate the proof using commutator vector operators acting on the conservation laws of the equation. This method can be generalized to obtain correlation estimates in all dimensions. In one dimension we use the Gauss‐Weierstrass summability method acting on the conservation laws. We then apply the two‐dimensional estimate to nonlinear Schrödinger equations and derive a direct proof of Nakanishi's H1 scattering result for every L2‐supercritical nonlinearity. We also prove scattering below the energy space for a certain class of L2‐supercritical equations. © 2009 Wiley Periodicals, Inc.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.329
Teacher spread0.271 · 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 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

Citations100
Published2009
Admission routes1
Has abstractyes

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