Scattering Field Solutions of Metasurfaces Based on the Boundary Element Method for Interconnected Regions in 2-D
Bibliographic record
Abstract
This article presents a method to determine the scattered electromagnetic (EM) fields in the interconnected regions with common metasurface boundaries. This method uses a boundary element method (BEM) formulation of the frequency domain version of Maxwell's equations, which expresses the fields present in a region due to surface currents on the boundaries. Metasurface boundaries are represented in terms of surface susceptibilities which when integrated with the generalized sheet transition conditions (GSTCs) gave rise to an equivalent configuration in terms of electric and magnetic currents. These representations are then naturally incorporated into the BEM methodology. Four examples are presented for EM scattering of a Gaussian beam to illustrate the proposed method. In the first example, a metasurface is excited with a diverging Gaussian beam, and the scattered fields are validated using a semianalytical method. The second example is concerned with a nonuniform metasurface modeling a diffraction grating, whose results were confirmed with a conventional finite-difference frequency-domain (FDFD) method. To illustrate the flexibility of the method, the third example uses a metasurface that implements a polarization rotator. Finally, a fully absorbing metasurface is simulated and compared to the FDFD simulations to emphasize the advantages of BEM method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 0.001 |
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.
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".