Comparative study of space-mapping-based optimization techniques for microwave design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Space mapping (SM) has been used in microwave engineering for over a decade and demonstrated as one of the most efficient simulation-driven design techniques available to date. By exploiting a fast and physically-based surrogate model, SM allows rapid optimization of an EM-simulated structure of interest (fine model). Several variations of SM algorithms have been proposed including recently introduced tuning space mapping (TSM) that can be even more efficient. However, TSM requires modification to the optimized structure (i.e., “cutting” and inserting of the tuning ports). Here, for the first time, a comparative study of various SM and TSM algorithms is carried out. Advantages and disadvantages of these techniques are discussed and illustrated through design examples. Recommendations for users of SM/TSM are given.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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