Coarse models for efficient space mapping optimisation of microwave structures
Bibliographic record
Abstract
It follows from both theoretical results and practical observations that the coarse model is one of the most critical components of the space mapping optimisation process, affecting both the algorithm's ability of finding a high-quality design, and its computational complexity. A good coarse model should be a good representation of the fine model and, at the same time, it should be computationally cheap. The first property not only ensures the quality of the final design but also good convergence properties of the algorithm, so it also affects the computational complexity of the optimisation process through reducing the number of fine model evaluations required to find the solution. The second property ensures that the overhead related to parameter extraction and surrogate optimisation is small or even negligible. This study discusses techniques for creating computationally cheap and reliable coarse models. The approaches the authors present include interpolated models, multi-coarse-model techniques and the use of built-in capabilities of the coarse model simulator. The authors provide examples involving microwave design optimisation problems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".