Rapid electromagnetic‐based microwave design optimisation exploiting shape‐preserving response prediction and adjoint sensitivities
Bibliographic record
Abstract
A new development of the shape‐preserving response prediction (SPRP) technique for microwave design optimisation is presented here. The original SPRP method is enhanced by employing low‐cost derivative information obtained through adjoint sensitivities. The authors propose using operator notation to simplify the SPRP surrogate description. The enhancement through sensitivity data is twofold: to ensure first‐order consistency between the SPRP surrogate and the high‐fidelity electromagnetic (EM) model under optimisation and to speed up the surrogate optimisation process. Fast surrogate optimisation allows us to use coarse‐discretisation EM simulations as an underlying low‐fidelity model and, therefore, efficiently apply SPRP to cases where reliable circuit models are not available (e.g. design of antenna structures). The proposed approach is demonstrated using a dielectric resonator filter and an ultra‐wideband monopole antenna. Comparison with three benchmark techniques, including the original SPRP methods, space mapping with sensitivity and direct optimisation of the high‐fidelity model, is also provided.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".