Feature‐based surrogates for low‐cost microwave modelling and optimisation
Bibliographic record
Abstract
A simple, reliable, and accurate surrogate modelling technique for microwave structures is presented. A hierarchical surrogate model is constructed using response surface approximations (RSAs) of suitable response features with embedded ‘visible’ knowledge. The authors describe and discuss a novel scanning algorithm for capturing these desired response features. They demonstrate that the dependence of the selected features on the designable parameters is much less non‐linear than that of the original responses taken as functions of the design parameters. They illustrate the steps of their algorithm using novel diagrams. They simplify the description of their modelling process by exploiting an operator notation. They discuss relationships between their method and other feature‐based approaches such as shape‐preserving response prediction (SPRP). They provide a wideband microstrip bandstop filter, a fourth‐order ring resonator bandpass filter and a microstrip bandpass filter with open stub inverter examples to demonstrate their approach. Using these examples, they compare their approach with two very different direct RSA methods (kriging and radial basis function) and an SPRP method. The models of all the filter examples are validated using design optimisation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".