Surrogate Model-Based Filter Optimization by a Field-Circuit Model Mapping
Bibliographic record
Abstract
In this article, an artificial neural network (ANN) model development technique is described for efficient and fast high-frequency structure design and optimization. Unlike the well-documented space mapping (SM) and aggressive SM technology, we map equivalent circuit (EC) model parameters to field model geometric parameters through neural modeling. First, a complete electromagnetic (EM) structure is segmented into a series of different discontinuities. Then, the EC model corresponding to each discontinuity is derived from a set of calibrated circuit parameters. Next, couplings of different orders between the discontinuities are represented as a part of ECs. Finally, the complete EC model of a full-wave EM structure is developed. All the circuit parameters are extracted against different combinations of critical geometric parameters of the target EM structure. This dataset is used to develop the ANN model for mapping the EC model parameters to EM model geometric parameters. At this stage, the circuit model can be used for optimization purposes. The optimized circuit parameters are then mapped back to the geometric parameters in connection with the predesignated performance. In this work, a dual-band resonant-aperture (RA) rectangular waveguide filter and a third-order nonradiative dielectric (NRD) waveguide filter are shown as examples to demonstrate the proposed methodology. Both examples show a good agreement between simulation and measurement results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".