Design of microwave structures with M<scp>EFISTO</scp>‐3<scp>D</scp>N<scp>OVA</scp>and M<scp>ATLAB</scp>optimization and neural network toolboxes
Bibliographic record
Abstract
Abstract This paper introduces two time‐domain field‐based optimization procedures for microwave engineering. The methods are built on the foundations of MATLAB's optimization and neural network toolboxes. The first procedure makes use of a direct connection linking MATLAB's optimization toolbox with MEFISTO‐3DNOVA. In this approach the field simulator acts as an objective function server for the optimization toolbox; these two programs work cooperatively with each other to tune the structure parameters to obtain a target response. The second procedure is an indirect optimization approach that makes use of MATLAB's neural network toolbox in conjunction with MEFISTO‐3DNOVAto create a neural network model to emulate the structure of interest; the resulting neural network model is then used an objective function server in a normal MATLABoptimization process. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".