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Record W2048265138 · doi:10.1109/tuffc.2009.1308

Approximate green's function representations for the analysis of SAW and leaky wave devices

2009· article· en· W2048265138 on OpenAlexaff
R.C. Peach

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2009
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsCOM DEV International
Fundersnot available
KeywordsSuperposition principleSlownessGreen's functionMathematical analysisExponential functionFunction (biology)MathematicsAiry functionRange (aeronautics)WavenumberMatrix (chemical analysis)PhysicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

The Green's function or boundary element method (BEM) is the preferred technique for rigorous SAW device analysis. However, because of its computational cost, its principal application is the analysis of mode propagation in periodic structures to determine parameters that can then be used in simplified coupling of modes (COM) or P-matrix models. In this paper, rigorous representations are derived that express the Green's function in terms of a continuous superposition of modes. The derivations include detailed analysis of the Green's function properties as a function of both frequency and wavenumber, and representations are obtained for both the slowness and spatial domains. Approximate forms are then generated by replacing the continuous mode superposition by a discrete one. The Green's function can be approximated to any required degree of accuracy, and the resulting approximations are applicable to any type of wave on any type of substrate. The long-range spatial components in the approximate forms are represented by exponential terms. The separable properties of these terms allow this class of approximation to be applied to general SAW and leaky wave device analysis in such a way that the computational effort increases only linearly with device size.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.595

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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 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

Citations3
Published2009
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency ControlSame topicAcoustic Wave Resonator TechnologiesFrench-language works237,207