Systematic Neuro-Transfer Function Parametric Modeling With a Compact Embedded Format
Bibliographic record
Abstract
This research proposes a systematic neuro-transfer function (neuro-TF) parametric modeling with a compact embedded format. Introducing transfer functions significantly enhances the capability of neural networks for electromagnetic (EM) parametric modeling. For modeling data based on vector fitting processing, the subtransfer function (sub-TF) response represented by each pole-residue pair exhibits different physical properties and data characteristics. Embedding the transfer function in the neural network enables good modeling accuracy for the strongly resonant sub-TF response, but for the nonstrongly resonant sub-TF response the poles/residues change abruptly as the geometrical parameters vary. This discontinuity issue of transfer function parameters for nonstrong resonance results in poor robustness and modeling accuracy. We propose a compact form of partially embedding the transfer function in neural networks to systematically solve this problem without introducing any other functions and structures. To accurately judge the embedding range, we propose an embedding range judgment algorithm based on resonance degree. We outline the training process and derive the corresponding derivative formula to expedite gradient-based training convergence. The proposed method uses a compact embedded format to achieve good modeling accuracy compared to existing neuro-TF methods, even including methods that introduce other functions and structures. Three modeling examples of microwave components verify the effectiveness and robustness of the proposed method.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".