Expanding the Validity Range of Neural Models for Bandpass Filters Using Double Mapping
Bibliographic record
Abstract
This article presents a novel method for extending the applicability range of neural network models (NNMs) developed within a specific, narrow range. This technique is particularly useful for applications where creating the NNM requires access to a large number of accurate data points, obtained through either electromagnetic (EM) simulations or experimental results. The proposed approach employs a double-mapping (DM) technique to expand the NNM’s range without generating many EM data points. Initially, the NNM is constructed using training data from the fine model (FM) within a defined range. The DM strategy is then used to generate additional data points, extending the NNM’s applicability without relying on time-consuming RF simulations or additional experimental measurements. To demonstrate the effectiveness of this approach, it is applied to NNM of a four-pole dielectric resonator (DR) filter and a patch antenna, successfully extending their original training range. The performance of the extended NNM is evaluated using the mean squared error (MSE), showing a significant improvement in prediction accuracy. It provides an efficient way to extend the validity range of the NNM without requiring many RF simulations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".