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Record W2773268878 · doi:10.1109/tap.2017.2781740

Novel Single-Source Surface Integral Equation for Scattering Problems by 3-D Dielectric Objects

2017· article· en· W2773268878 on OpenAlexafffund
Farhad Sheikh Hosseini Lori, Anton Menshov, Reza Gholami, Jamiu Mojolagbe, Vladimir Okhmatovski

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntegral equationElectric-field integral equationScatteringDiscretizationMathematical analysisVolume integralSummation equationSurface integralSuperposition principlePhysicsMethod of moments (probability theory)Laplace's equationComputational electromagneticsElectromagnetic wave equationMathematicsBoundary value problemElectromagnetic fieldOpticsOptical fieldQuantum mechanics

Abstract

fetched live from OpenAlex

A new single-source integral equation is proposed for the solution of electromagnetic wave scattering problems. The traditional volume electric field integral equation is reduced to the new single-source surface integral equation by representing the electric field inside the scatterer as a superposition of spherical waves emanating from its boundary. Such new integral equation formulation has been previously developed for the scalar and vector cases of 2-D scattering problems. In this paper, the 3-D form of this new single-source surface integral equation for scattering on homogeneous nonmagnetic dielectrics is proposed. Detailed description of the method of moments (MoMs) discretization and its resultant matrices is presented. In order to validate the new integral equation formulation and verify the accuracy of its MoMs discretization, its solution is compared against the analytical Mie series solution and fields computed using the commercial electromagnetic analysis software.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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

Citations32
Published2017
Admission routes2
Has abstractyes

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