Recent advances in<scp>knowledge‐based</scp>model structure optimization and extrapolation techniques for microwave applications
Bibliographic record
Abstract
Abstract Artificial neural network modeling techniques have been recognized as important vehicles in the microwave computer‐aided design (CAD) area in addressing the growing challenges of designing next generation microwave device, circuits, and systems. This article provides an overview of recent advances in knowledge‐based neural network model generation and extrapolation techniques for microwave applications. We first introduce the unified knowledge‐based neural network structure optimization technique. Using the distinctive property for feature selection ofl1optimization, this unified modeling technique efficiently determines the type and topology of the mapping structure in a knowledge‐based model. This knowledge‐based model structure optimization technique is more flexible and systematic, and can further speed up the knowledge‐based neural model development. As a further advancement, we also discuss the advanced multi‐dimensional extrapolation technique for neural‐based microwave modeling. The purpose is to make the neural network model can be reliably used not only inside the training range but also outside the training range. Multi‐dimensional cubic polynomial extrapolation formulation and optimization over grids outside the training range are utilized to make neural models more robust and reliable when they are used outside the training range. The validity of these techniques is demonstrated by microwave modeling 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".