Electromagnetic Sensitivity Analysis of Scattering Parameters Based on the FDFD Method
Bibliographic record
Abstract
We propose a novel technique to compute response gradients (Jacobians) from frequency-domain field solutions provided by high-frequency electromagnetic (EM) simulations. It is based on our recently developed self-adjoint sensitivity-analysis (SASA) approach where only one EM simulation suffices to obtain both the responses and their gradients in the optimizable-parameter space. Our novel technique exploits the computational efficiency of the SASA while adapting it to the system equations of the frequency-domain finite-difference (FDFD) method. There are three major advantages to this development: (a) the Jacobian computation is completely independent of the simulation engine, its grid and its system equations; (b) the implementation is straightforward and in the form of a post-processing algorithm operating on the exported field solution; (c) it is computationally very efficient-memory and computer-time requirements are negligible compared to those of the simulation itself. The proposed technique drastically reduces the overall time required by field-based optimization processes arising in design and inverse problems as compared to response Jacobians computed via response-level finite differences or parameter sweeps. Its accuracy is verified by comparisons with response-level central finite-difference derivative estimates.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".