Super‐Resolution Neural Networks for High‐Contrast Electromagnetic Scattering Problems
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Bibliographic record
Abstract
ABSTRACT This letter proposes a super‐resolution (SR) neural network model for high‐contrast electromagnetic scattering problems. The model is designed to predict fine‐grid field distributions based on low‐cost coarse‐grid simulations. By integrating a spatial channel attention mechanism, the model enhances accuracy in capturing field discontinuities induced by strong scatterers. Additionally, a residual‐in‐residual architecture is incorporated to provide the network with sufficient depth for effective correction of dispersion errors. The efficiency and accuracy of the proposed model have been validated through numerical experiments. Comparative evaluations with a recently proposed electromagnetic SR network, supplemented by rigorous ablation studies, further demonstrate the superior performance of our approach in high‐contrast scenarios.
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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