Parametric Modeling of Microwave Components Using Adjoint Neural Networks and Pole-Residue Transfer Functions With EM Sensitivity Analysis
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Bibliographic record
Abstract
This paper proposes a pole-residue-based adjoint neuro-transfer function (neuro-TF) technique with electromagnetic (EM) sensitivity analysis for parametric modeling of EM behavior of microwave components with respect to changes in geometrical parameters. The purpose is to increase model accuracy by utilizing EM sensitivity information and to speed up model development by reducing the number of training data required for developing the model. The proposed parametric model consists of original and adjoint pole-residue based neuro-TF models. New formulations are derived for calculating the second-order derivatives for training the adjoint pole-residue-based neuro-TF model. An advanced pole-residue tracking technique is proposed to exploit the sensitivity information to track the splitting of poles as geometrical parameters change. This pole-residue tracking technique allows the model to bridge the differences of the orders of transfer function over different regions of the geometrical parameters, and ultimately form smooth and continuous functions between the pole/residues and the geometrical variables. The proposed technique addresses the challenges of tracking pole splitting when training data are limited. By exploiting the sensitivity information, the proposed technique can speed up the model development process over the existing pole-residue parametric modeling method which does not use sensitivity analysis.
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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 it