Robust neural based microwave modelling and design using advanced model extrapolation
Bibliographic record
Abstract
For the first time, the issue of using neural-based microwave models far outside their training range is directly addressed. A standard neural model is meaningful only outside the particular range of inputs for which it is trained, and becomes unreliable when used outside this range. This paper presents a robust neural modelling technique incorporating advanced extrapolation to address this problem. A new process is incorporated in training to formulate a set of base points to represent a regular or irregular training region. An adaptive base point selection method is developed to identify the most significant subset of base points upon any given value of model input. This method is combined with quadratic extrapolation utilizing neural network outputs and their derivatives. The proposed technique is demonstrated by examples of neural based design solution space analysis of coupled transmission lines and neural based behaviour modelling and simulation of power amplifiers. It is demonstrated that the proposed technique allows the neural based microwave models to be used far beyond their original training range.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".