Optimization of Electromagnetic Metasurface Parameters Satisfying Far-Field Criteria
Bibliographic record
Abstract
Electromagnetic metasurfaces offer the capability to realize arbitrary power-conserving field transformations. These field transformations are governed by the generalized sheet transition conditions, which relate the tangential fields on each side of the surface through the surface parameters. Ideally, designers would solve for the surface parameters based on their application-specific far-field criteria. However, determining the surface parameters for these criteria is challenging without knowledge of the tangential fields on each side of the surface, which are not unique for a given far-field pattern. Current designs are generally restricted to analytical examples where the tangential fields can be solved for, or determined viaad hocmethods, although there has been recent work to circumvent this. This article presents an optimization scheme, which determines surface parameters, such as electric impedance, magnetic admittance, and magnetoelectric coupling, satisfying far-field constraints, such as beam level, sidelobe level, and null locations. The optimization is performed using a method of moments-based model incorporating edge effects and mutual coupling. The surface parameters are optimized for using the alternating direction method of multipliers. Examples of this optimization scheme performing multicriteria pattern forming, extreme angle small surface refraction, and Chebyshev-like beamforming are presented.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".