Bibliographic record
Abstract
A powerful concept in neuromodeling of microwave circuits based on Space Mapping technology is described. The ability of Artificial Neural Networks (ANN) to model high-dimensional and highly nonlinear problems is exploited in the implementation of the Space Mapping concept. By taking advantage of the vast set of empirical models already available for many microwave structures, Space Mapping based neuromodels decrease the number of EM simulations for training, improve the generalization and extrapolation performance and reduce the complexity of the ANN topology with respect to the conventional neuromodeling approach. Five innovative techniques are proposed to create Space Mapping based neuromodels for microwave circuits: Space Mapped Neuromodeling (SMN), Frequency-Dependent Space Mapped Neuromodeling (FDSMN), Frequency Space Mapped Neuromodeling (FSMN), Frequency Mapped Neuromodeling (FMN) and Frequency Partial-Space Mapped Neuromodeling (FPSM). Excepting SMN, all these approaches establish a frequency-sensitive neuromapping to expand the frequency region of accuracy of the empirical models already available for microwave components that were developed using quasi-static analysis. We contrast our approach with the conventional neuromodeling approach employed in the microwave arena, as well as with other state-ofthe-art neuromodeling techniques. We use Huber optimization to efficiently train the simple ANN that implements the mapping in our SM-based neuromodels. The five space mapping based neuromodeling techniques are illustrated by two case studies: a microstrip right angle bend and a hightemperature superconducting (HTS) quarter-wave parallel coupled-line microstrip filter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".