Transmissive Metasurface Synthesis From Far-Field Masks Using Unsupervised Learning
Bibliographic record
Abstract
Designing lossless and passive transmissive metasurfaces requires the knowledge of tangential electromagnetic fields on the metasurface aperture and the enforcement of local power conservation (LPC). In design scenarios where the desired radiation patterns are specified by lower and upper masks, we have developed a deep learning (DL) approach to infer the required metasurface aperture fields, while favouring the LPC constraint. In the cases examined here, the aperture fields obtained through the DL approach lead to power patterns that exhibit good alignment with the specified far-field masks, while adhering to the LPC constraint. When simulating the resulting metasurfaces using impedance sheets, this alignment slightly degrades, partly due to the non-zero thickness of the metasurface and the local periodicity assumption used in the unit cell design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".