Inferring Surface Susceptibilities for Mask-Based Metasurface Beam Shaping Using Deep Learning
Bibliographic record
Abstract
This work presents a deep learning-based framework for the macroscopic design of transmissive metasurfaces to enable the transformation of a given incident wave into a farfield power pattern that resides within a user-defined mask. This neural network then directly generates the required metasurface properties, known as surface susceptibilities, that (i) satisfy reciprocity, losslessness, and passivity conditions, and (ii) result in high transmission efficiency. An important part of this neural network is a fast metasurface forward solver, known as the implicit IE-GSTC method, that combines the generalized sheet transition conditions (GSTCs) with the integral equation (IE) formulation to calculate the metasurface scattering response. The achieved macroscopic designs show promising results under the ideal zerothickness scenario. However, the far-field performance degrades as the zero-thickness model is converted into a finite-thickness three-layered model. This discrepancy may be attributed to the assumption of perpendicular wave propagation in the generation of our utilized finite-thickness metasurface model.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".