Recent Advances in Novel Training Approaches for Microwave Parametric Modeling Using Padé via Lanczos and EM Sensitivities
Bibliographic record
Abstract
This paper provides the recent advance in training approachs for microwave parametric modeling of passive components with changes of geometrical variables combining electromagnetic (EM) sensitivities and matrix Padé via Lanczos (MPVL). In this paper, the pole-zero-gain transfer function is used as the surrogate model of EM response of microwave components versus frequency. The relationships between geometrical parameters and the gain/zeros/poles are learned directly by artifical neural networks (ANN). MPVL algorithm is performed to compute/recompute the zeros/poles corresponding to different geometric parameters to generate training data. After recomputations, the indices of the zeros/poles not always have right correspondences with original indices, leading to an unstable prediction of the zeros/poles when geometrical parameters have a new change. A novel sensitivity-based zero/pole-matching algorithm is used to achieve the right correspondences between the zeros/poles at different geometrical variables. This method uses the EM sensitivities as an aid information for the moving direction of the zeros/poles, to match the zeros/poles and geometrical parameters that changes each time. Using the matched zeros/poles to train the neural network has reliable and fast predictions for large geometrical variations, which increases the robustness and accuracy of the surrogate model. Compared with those existing algorithms, this method can train a more accurate model in applications involving large geometrical variations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".