Novel Dominant-Order-Based Pole/Zero Matching Technique for MOR-Based Neuro-DTF Parametric Modeling of Microwave Components
Bibliographic record
Abstract
The model order reduction (MOR)-based modeling method combining neural networks and the transfer function (neuro-TF) is widely used for efficient parametric modeling of electromagnetic (EM) behaviors in microwave components. However, the order of MOR is usually required to be much higher than the actual order of the microwave components to achieve accurate EM solutions. This extra order by MOR results in a high possibility of uncertainty and discontinuity of the extracted extra poles/zeros with respect to geometrical variations for neuro-TF model development. To address the above issue, this article proposes a novel dominant-order-based pole/zero matching technique for parametric modeling, incorporating dominant-order-based transfer functions and neural networks (short for neuro-DTF). The proposed dominant-order-based pole/zero matching technique is to identify dominant poles and zeros through a novel adaptive variable-order reduction process. Dominant poles and zeros are screened and retained by matching successive high- and low-order reduced systems, and nondominant poles and zeros are transferred into a complex exponential function. By combining the dominant poles and zeros with the complex exponential function, the dominant-order-based transfer function is obtained. The core objective is to retain poles and zeros with dominant influence on key frequency bands while simplifying nondominant dynamics. The proposed technique effectively eliminates pole/zero mismatch issues caused by the uncertainty and discontinuity of the extracted extra poles/zeros with respect to geometrical variations. Compared to conventional MOR-based neuro-TF parametric modeling, the proposed neuro-DTF method, incorporating the dominant-order-based pole/zero matching technique, achieves higher accuracy and robustness, especially under large geometrical parameter variations. Its effectiveness is verified through two microwave examples.
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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) | 0.002 | 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".