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Record W2021003175 · doi:10.1115/imece2010-37041

Investigation of the Transition Character for Duffing System by Employing Periodicity Ratio and Lyapunov Exponents

2010· article· en· W2021003175 on OpenAlexaff
Lu Han, Liming Dai, Huayong Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicAdvanced Differential Equations and Dynamical Systems
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsLyapunov exponentDuffing equationDivergence (linguistics)Nonlinear systemCharacter (mathematics)MathematicsTransition (genetics)Statistical physicsCHAOS (operating system)Convergence (economics)Mathematical analysisApplied mathematicsPhysicsComputer scienceQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

The transition characters from periodicity to chaos and periodicity to quasi-periodicity have been detected for a Duffing equation which is a representative nonlinear dynamic system who has complicated, diverse transition characters. Specifically, Lyapunov exponents and periodicity ratio method are both employed to diagnose the transition routes. By combining the two methods together, it is very efficient to classify different types of transition of a nonlinear differential system. It can be found that the transition routes from periodicity to chaos can be more divergent than other routes from periodicity to quasi-periodicity. Therefore, some predications can be made about the occurrence of the chaos by observing the divergence or convergence of the transition routes. Further more, the new symmetrical transition characters from periodicity to quasi-periodicity can be displayed in terms of the periodicity Ratio. Comparing to Lyapunov Exponents, the Periodicity Ratio can disclose more detailed transition information.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.261
Teacher spread0.231 · 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 designBench or experimental
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

Citations0
Published2010
Admission routes1
Has abstractyes

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