Experimental Evaluation of a Deep Learning Approach to Transmissive Metasurface Design for Mask-Based Power Pattern Synthesis
Why this work is in the frame
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Bibliographic record
Abstract
An end-to-end deep-learning-based approach is considered for the design of lossless and passive transmissive metasurfaces that transform known incident electromagnetic waves into new ones whose far-field power patterns reside within user-defined lower and upper masks. The far-field pattern obtained from this design approach is evaluated using two simulation strategies relying on zero-thickness and finite-thickness metasurface models, as well as experimental measurements performed in a planar near-field antenna range. It is shown that the adherence of the achievable power pattern to the desired far-field masks deteriorates as we transition from the simulated zero-thickness model to the simulated finite-thickness model and eventually to the fabricated metasurface that is evaluated experimentally. Finally, the reflectivity of the fabricated metasurface is evaluated using the time gating feature of a vector network analyzer, confirming the low level of reflectivity under normal plane wave illumination for the fabricated transmissive metasurface.
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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) | 0.000 | 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 it