Sensitivity Analysis for a Fifth-Order Waveguide Filter Using MOR-Based EM Sensitivity Analysis Method
Bibliographic record
Abstract
Efficient adjoint electromagnetic (EM) sensitivity evaluation plays a crucial role in the design and optimization process of microwave devices. Recent work demonstrates that combining adjoint sensitivity analysis with fast frequency sweep provides a notable speed advantage over traditional discrete sweeps. This paper reviews the latest model order reduction (MOR)-assisted adjoint and self-adjoint sensitivity approaches tailored for fast frequency sweeps. The formulation streamlines the derivation of adjoint EM sensitivities, reducing both the number of forward/backward (F/B) substitutions and the burden of matrix operations. Moreover, the self-adjoint formulation can readily integrate with different moment-matching-based MOR techniques. In this paper, we also apply these methods to the EM sensitivity analysis of a fifth-order waveguide filter, demonstrating the accuracy of the calculated results and their high efficiency compared with other traditional methods.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".