Advanced Autoencoder Transfer Function Parameter Extraction Technique for Neuro-TF Parametric Modeling of Microwave Components
Bibliographic record
Abstract
Recently, neuro-transfer function (neuro-TF) has become a recognized method for electromagnetic (EM) parametric modeling. The existing neuro-TF methods use the vector fitting technique to perform transfer function (TF) parameter extraction, commonly encountering nonsmoothness and discontinuity issues for the extracted TF parameters with respect to geometrical parameters. This letter proposes an advanced autoencoder TF parameter extraction technique for neuro-TF parametric modeling of microwave components. In the proposed technique, the autoencoder is introduced to extract a set of TF parameters as TF parameters from the$S$-parameters with the encoder part and generate a decoder function as the TF in the original neuro-TF model. The TF parameters extracted using the proposed technique behave much smoother than the TF parameters extracted using traditional vector fitting. Meanwhile, the proposed technique avoids the discontinuity problem caused by vector fitting in the standard neuro-TF method. Parametric modeling using the smooth TF parameters can thus have higher accuracy than modeling with nonsmooth TF parameters. The proposed technique is demonstrated by two examples of EM parametric modeling of microwave components.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".