Surface-Volume-Surface Electric Field Integral Equation for Solution of Scattering Problems on 3-D Dielectric Objects in Multilayered Media
Bibliographic record
Abstract
Generalization of the surface-volume-surface electric field integral equation (SVS-EFIE) for the solution of electromagnetic scattering problems on 3-D dielectric objects embedded in multilayered media is proposed. While having only a single unknown surface current density on the boundary of the scatterer, the SVS-EFIE also features only electric field dyadic Green's functions in its integral operators. This property in conjunction with Michalski-Zheng's formulation of the multilayered media electric field Green's function allows for the formulation of SVS-EFIE in the mixed potential form for the solution of the scattering problems in layered media. In the proposed method of moments (MoM) discretization scheme, the gradient and divergence operators associated with the electric field Green's function are shifted to the basis and test functions of the discretized integral operators. As a result, the proposed formulation features no derivatives acting on the components of the layered media dyadic Green's function, hence, substantially alleviating the numerical evaluation of the pertinent Sommerfeld integrals. The proposed MoM formulation scalarizes reaction integrals containing the multilayered media dyadic Green's function through the use shape function-based definition of the basis and test functions. The resultant MoM integrals feature no singularities stronger than 1/R. The validation of the proposed SVS-EFIE formulation and its MoM discretization is performed through a comparison of the computed fields against the fields produced using commercial electromagnetic analysis software.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".