Statistical Space Mapping Approach for Large-Signal Nonlinear Device Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An efficient nonlinear statistical modeling technique for microwave devices is presented. A new statistical space mapping concept is introduced that can expand a large-signal nominal model into a large-signal statistical model. The nominal model is extracted or trained from one complete set of large-signal data. The statistical property is achieved by a dynamic mapping between the behavior of the nominal model and that of the statistical samples of a given population of devices. The parameters in the mapping, which are statistical parameters, can be extracted from DC and small-signal S-parameter data of many device samples. Example of a MESFET device modeling and its use in statistical design of a three-stage amplifier circuit demonstrate that the statistical space-mapped model can approximate the large-signal statistical characteristics using only one set of large-signal data. It helps to efficiently develop large-signal statistical models while reducing the expense of otherwise massive large-signal measurements for many devices
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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