Sensitivity Analysis With Full-Wave Electromagnetic Solvers Based on Structured Grids
Bibliographic record
Abstract
Recently, we proposed a novel technique for design sensitivity analysis of high-frequency structures with respect to localized perturbations in conductive parameters. Here, we generalize the technique to include shape and material variations and utilize the response sensitivities in gradient-based optimization. Our technique belongs to the class of adjoint-variable methods. Thus, it computes the response and its gradient with only two electromagnetic (EM) simulations-of the original and the adjoint problems-regardless of the number of design parameters. For the first time, adjoint sensitivities with respect to conductive, dielectric-magnetic material and shape perturbations are computed via EM solvers on structured grids. Our approximate sensitivity analysis does not require analytical derivatives of the system matrix. This makes the technique versatile and easy to implement. The technique defaults to exact sensitivities with analytical system matrix derivatives when global design parameters are being perturbed. We discuss the accuracy of the approximate sensitivities, as well as the practicality of the exact sensitivities in specific design problems. We also discuss implementations in gradient-based optimization and illustrate them through simulation and design with the frequency domain transmission line method (FD-TLM).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".