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Record W2726600812 · doi:10.1109/nemo.2017.7964220

Higher order method of moments solution of the new vector single-source surface integral equation for 2D TE scattering by dielectric objects

2017· article· en· W2726600812 on OpenAlexaff
Farhad Sheikh Hosseini Lori, Mohammad Shakander Hosen, Vladimir Okhmatovski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIntegral equationElectric-field integral equationScatteringMathematical analysisVolume integralMethod of moments (probability theory)Superposition principlePhysicsCylinderSurface integralSurface (topology)Summation equationField (mathematics)Mie scatteringDielectricMathematicsOpticsGeometryQuantum mechanicsLight scattering

Abstract

fetched live from OpenAlex

The traditional volume electric field integral equation can be reduced to a single-source surface integral equation by representing the electric field inside the scatterer as a superposition of elementary waves emanating from its boundary. Such new integral equation formulation has been previously developed for both the TM- and TE-waves scattering by 2D dielectric cylinders. This paper introduces a higher order method of moments solution of this new vector single-source surface integral equation in the case of TE-waves scattering. The error-controlled method of moments solution is validated against analytic Mie series solution for a circular cylinder with exact geometrical representation. Numerical solutions with maximum relative error in the computed fields not exceeding 10-5are achieved, hence, validating the rigorous nature of the new single source integral equation formulation.

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)0.001
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.274
Teacher spread0.253 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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