Rapid multi‐objective design optimisation of compact microwave couplers by means of physics‐based surrogates
Bibliographic record
Abstract
The authors introduce a methodology for fast multi‐objective design optimisation of miniaturised microwave couplers. The approach exploits the surrogate‐based optimisation paradigm with an underlying low‐fidelity model constructed from an equivalent circuit of the structure under consideration, corrected through implicit and frequency space mapping. A fast prediction tool obtained this way is subsequently optimised by a multi‐objective evolutionary algorithm to identify an initial approximation of the Pareto front, that is, a set of designs representing the best possible trade‐offs between conflicting objectives. The correction/optimisation of the surrogate is then iterated by design space confinement and segmentation based on a Pareto set representation obtained thus far in the process. This aims at improving the surrogate model accuracy in the vicinity of the Pareto‐optimal solutions. The technique is demonstrated by two design examples of compact rat‐race couplers. Experimental validation is also provided.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".