Impedance-Feature-Based Gauss–Newton Optimization Incorporating MOR and FFS Sensitivity for Waveguide Filters
Bibliographic record
Abstract
UsingS-parameter features to assist or even directly drive the optimization of microwave filters (the latter often called as cognition-driven optimization) has proven to be an effective approach when the initial design is suboptimal. However, the robustness ofS-parameter feature extraction tends to degrade significantly when the initial design deviates further from the target. Recently, an impedance-feature-based cognition-driven space mapping (SM) optimization method has been proposed to address this limitation, which demonstrates superior robustness in the case of poor initial designs and provides more accurate optimization guidance compared withS-parameter-based methods. This article proposes an impedance-feature-based Gauss–Newton optimization algorithm incorporating model order reduction (MOR) and fast frequency sweep (FFS) sensitivity. The impedance features are extracted directly through an MOR process based on the finite element method (FEM) and the inherent properties of impedance. Subsequently, the physical expressions of the derivatives of impedance features under the FEM are derived. Due to the severe numerical deviations caused by the high sensitivity of impedance features near resonance, a modified fast derivative formula is proposed. In combination with the derived FFS sensitivity expression of the impedance parameters, the proposed method achieves sensitivity calculations with the same accuracy as finite-difference derivatives but with significantly improved efficiency. Based on the obtained sensitivity information of the impedance features, a trust-region-based Gauss–Newton optimization formulation is developed, enabling faster and more robust optimization compared with previous work. The proposed approach is validated and compared through two illustrative examples.
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 | 0.001 | 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 | 0.000 | 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".