Recent Advances and Future Trends in Neuro-Tffor EM Optimization
Bibliographic record
Abstract
Artificial neural networks (ANNs) are important tools to perform electromagnetic (EM) parametric modeling and design optimization for microwave structures. Recently, an advanced knowledge-based ANN technique, called neuro-transfer function (short for neuro-TF), has been developed. In the neuro-TF method, transfer function is used as the prior knowledge that expresses the highly nonlinear EM responses versus frequency. Using this transfer function knowledge, the remaining relationships of the transfer function parameters versus geometrical variables are less nonlinear for ANN to learn, resulting neuro-TF to be more accurate and robust. The trained neuro-TF model can be used as surrogate model to perform fast surrogate-based EM design optimization. Future trends for neuro-TF technique can be exploring new ways to incorporate different transfer functions into various intermediate parts of neural structures and training, and combining with various generic optimization methods. Furthermore, incorporation of EM internal formulations into neuro-TF may lead to new solutions to conquer the expense of EM design optimization of microwave structures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".