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Frequency Combs and Stochastic Responses in A Curved Microbeam Featuring 2:1 Internal Resonance

2023· article· en· W4399987294 on OpenAlexaff
Penghui Song, Sasan Rahmanian, Jiahao Wu, Eihab Abdel‐Rahman, Lei Shao, Wenming Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrobeamStochastic resonanceResonance (particle physics)PhysicsComputer scienceOpticsNoise (video)Atomic physicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we investigate the generation of frequency combs, dense equidistant spectral lines, in a MEMS device featuring a 2:1 internal resonance. We have observed the emergence of two distinct patterns of frequency combs and a chaotic regime under different excitation conditions. We have conducted an in-depth analysis of these novel dynamic phenomena, proposing a mechanism centered on the competition of two types of nonlinear wave mixing processes in the parametric excitation, resulting in a transition in the response state. Moreover, we have analyzed the stochastic dynamics of the peculiar comb-like responses within the chaotic region. This work further delves into the intricate dynamic behaviors of a modal interaction device within the frequency comb regime and provides guidance for the design of frequency comb generators based on micro-electromechanical systems (MEMS).

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.019
GPT teacher head0.254
Teacher spread0.235 · 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
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

Citations1
Published2023
Admission routes1
Has abstractyes

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