Robust Parameter Extraction Technique Based on Complex-Frequency-Domain EM Behavior for Neuro-TF Modeling of Microwave Filters
Bibliographic record
Abstract
The neuro-transfer function (Neuro-TF) modeling approach has been widely used to accelerate the electromagnetic (EM) modeling and optimization process. In the standard Neuro-TF approach, the extracted transfer function (TF) parameters (e.g., poles/residues or zeros/poles) are complex values in the real frequency domain (i.e., the$j\omega $-axis). Since the complex TF parameters are in the Laplace domain, the standard Neuro-TF approach inherently has a nonuniqueness parameter extraction issue with respect to geometrical parameters change. This article addresses this issue and proposes a robust parameter extraction technique based on complex-frequency-domain (CFD) EM behavior for Neuro-TF modeling of microwave filters. We approximately consider the real domain of CFD as a loss in this article. Therefore, we want to extract TF parameters not only from the frequency domain but also from the loss domain. In the proposed technique, we introduce the model-order reduction (MOR) technique to expedite the parameter extraction process, enabling fast frequency and loss sweeps simultaneously. The introduction of the MOR technique avoids the discrete solving of the EM response of each loss in the CFD, thus expediting the proposed parameter extraction process. The extracted parameters using the proposed technique have better correspondence with the information of CFD, improving the smoothness of extracted TF parameters. Therefore, the proposed technique enhances the robustness of TF parameter extraction and subsequently improves the accuracy of the Neuro-TF modeling over a relatively wide geometrical range. This article utilizes five microwave filter examples to demonstrate the advantages of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".