A Space-Mapping Approach to Microwave Device Modeling Exploiting Fuzzy Systems
Bibliographic record
Abstract
We present a novel surrogate modeling methodology based on a combination of space mapping and fuzzy systems. Fine model data, the so-called base set, is assumed available in the region of interest. Although we do not assume any particular location of the base points, it is preferable that they form a uniform mesh. The standard space-mapping surrogate is established using available fine model data. The fuzzy system is then set up to interpolate the differences between the space-mapping surrogate and the fine model at all base points. Our new methodology offers significant advantages with respect to some of the previous space-mapping approaches to modeling, which are: (1) it handles any base set and (2) the number of space-mapping parameters does not limit the accuracy of the surrogate. Moreover, it exhibits comparable or better accuracy than the recently published modeling technique utilizing space mapping and radial basis functions. We also consider a hierarchical fuzzy space-mapping modeling, which relies on a fuzzy interpolation of space-mapping parameters and subsequent fuzzy interpolation of the residuals between the fine and surrogate model. Examples demonstrate the robustness of our approach and give a comparison with other space-mapping-based modeling techniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".