IE-GSTC Metasurface Field Solver Using Surface Susceptibility Tensors With Normal Polarizabilities
Bibliographic record
Abstract
An integral equation (IE)-based electromagnetic field solver using metasurface susceptibility tensors is proposed and validated using a variety of numerical examples in 2-D. The proposed method solves for fields generated by the metasurface, which is represented as spatial discontinuities satisfying the generalized sheet transition conditions (GSTCs), and described using tensorial surface susceptibility densities,$\bar {\bar {\chi }}$. For the first time, the complete tensorial representation of susceptibilities is incorporated in this integrated IE-GSTC framework, where the normal surface polarizabilities and their spatial derivatives along the metasurface are rigorously taken into account. The proposed field equation formulation further utilizes a local coordinate system, which enables modeling metasurfaces with arbitrary orientations and geometries. The proposed 2-D boundary element method BEM-GSTC framework is successfully tested using a variety of examples, including infinite and finite-sized metasurfaces, periodic metasurfaces, and complex shaped structures, showing comparisons with both analytical results and a commercial full-wave solver. It is shown that the zero-thickness sheet model with complete tensorial susceptibilities can very accurately reproduce the macroscopic fields, accounting for their angular field scattering response and the edge diffraction effects in finite-sized surfaces.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".