Advanced Surrogate-Based EM Optimization Using Complex Frequency Domain EM Simulation-Based Neuro-TF Model for Microwave Components
Bibliographic record
Abstract
Surrogate-based electromagnetic (EM) optimization techniques have become popular for microwave design. Neuro-transfer function (neuro-TF) is one of the effective surrogates to represent the EM behaviors for design optimization. In this article, we propose an advanced surrogate-based EM optimization using neuro-TF developed by complex frequency domain (CFD)-based EM simulations. For the first time, we develop and introduce the CFD-based EM simulation using fast frequency sweep. We propose a novel transfer function zero/pole extraction technique based on the magnitude of the S-parameter of CFD-based EM simulations. Two-step training process is used for developing the neuro-TF surrogate model with the extracted zero/poles. Using the proposed zero/pole extraction technique, the developed neuro-TF surrogate model can have larger geometrical range than that using vector fitting. Consequently, the surrogate-based EM optimization using neuro-TF developed by CFD-based EM simulation can achieve a speedup over the standard neuro-TF optimization. Two examples of EM optimizations of microwave components are used to demonstrate the proposed technique.
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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".