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Overview of Surface-Volume-Surface Electric Field Integral Equation Formulations for 3-D Composite Metal-Dielectric Objects

2020· article· en· W3041066602 on OpenAlexaff
Reza Gholami, Shucheng Zheng, Vladimir Okhmatovski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsElectric-field integral equationDielectricIntegral equationSurface (topology)ScatteringComposite numberSurface integralVolume integralElectric fieldMathematical analysisField (mathematics)MathematicsMaterials sciencePhysicsGeometryOpticsComposite materialPure mathematicsOptoelectronicsQuantum mechanics

Abstract

fetched live from OpenAlex

The Surface-Volume-Surface Electric Field Integral Equation (SVS-EFIE) has been recently generalized to solution of general scattering and radiation problems for 3D composite objects. These objects can be formed by multiple piece-wise homogeneous dielectric regions which do or do not share common boundaries. Generalization to the composite objects formed by metal and piece-wise homogeneous dielectric regions which share common boundaries has also been demonstrated. Since the SVS-EFIE formulation utilizes only the electric field dyadic Green’ s functions, it can be extended to the case of composite objects situated in non-magnetic planar multilayered media by casting its operators into the mixed-potential form using classical Michalski-Zheng’ s approach. Examples of the above SVS-EFIE formulations are summarized in the paper.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.035
GPT teacher head0.277
Teacher spread0.242 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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