Adjoint EM Sensitivity Analysis for Fast Frequency Sweep Using Matrix Padé via Lanczos Technique Based on Finite-Element Method
Bibliographic record
Abstract
Sensitivity analysis is important for electromagnetic (EM)-based design. The existing adjoint EM sensitivity analysis methods have to solve large systems of EM equations repetitively for different frequencies. This article addresses this situation and proposes to speed up the EM sensitivity analysis over a frequency range by solving EM equations at only a single frequency. A new adjoint EM sensitivity analysis algorithm for the fast frequency sweep using the matrix Padé via Lanczos (MPVL) technique based on the finite-element method (FEM) is proposed in this article. MPVL is incorporated to relate the information of one frequency to the information of multiple frequencies. A large system of EM equations is then solved at a single frequency to predict the sensitivity information for the entire frequency band. Adjoint formulations are further derived to avoid the effect of the number of design variables. The adjoint EM sensitivity analysis using the proposed technique can obtain the same accuracy as the existing techniques while taking less time by avoiding repetitively solving large systems of EM equations for different frequencies and different design variables. The proposed technique is demonstrated by three EM examples of microwave components.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".