MétaCan
Menu
Back to cohort
Record W4298861577 · doi:10.48550/arxiv.1111.3865

Efficient determination of critical parameters of nonlinear\n Schr\\"{o}dinger equation with point-like potential using generalized\n polynomial chaos methods

2011· preprint· en· W4298861577 on OpenAlexaff
Debananda Chakraborty, Jae‐Hun Jung, Emmanuel Lorin

Bibliographic record

VenuearXiv (Cornell University) · 2011
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsSolitonMathematicsNonlinear systemConvergence (economics)PolynomialMathematical analysisCritical point (mathematics)AmplitudeSpace (punctuation)Nonlinear Schrödinger equationSpectral methodPolynomial chaosApplied mathematicsSchrödinger equationPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

We consider the nonlinear Schr\\"{o}dinger equation with a point-like source\nterm. The soliton interaction with such a singular potential yields a critical\nsolution behavior. That is, for the given value of the potential strength and\nthe soliton amplitude, there exists a critical velocity of the initial soliton\nsolution, around which the solution is either trapped by or transmitted through\nthe potential. In this paper, we propose an efficient method for finding such a\ncritical velocity by using the generalized polynomial chaos method. For the\nproposed method, we assume that the soliton velocity is a random variable and\nexpand the solution in the random space using the orthogonal polynomials. The\nproposed method finds the critical velocity accurately with spectral\nconvergence. Thus the computational complexity is much reduced. Numerical\nresults for the smaller and higher values of the potential strength confirm the\nspectral convergence of the proposed method.\n

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.262
Teacher spread0.169 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicAdvanced Fiber Laser TechnologiesFrench-language works237,207