Adjoint Sensitivity Analysis of Dielectric Discontinuities Using FDTD
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Bibliographic record
Abstract
Abstract We propose an accurate central adjoint variable method (CAVM) for estimating objective function sensitivities related to dielectric discontinuities with structured-grid finite difference time domain (FDTD). Our novel approach features accuracy comparable to that of the central finite difference approximation at the response level. Using only two simulations, of the original and the adjoint EM structures, the sensitivities with respect to all the designable parameters are obtained regardless of their number. Our approach uses the same update equations of the conventional FDTD for the adjoint problem which simplifies the implementation. The proposed technique is extended to evaluate the sensitivities of the S-parameters of multi-port electromagnetic structures. Very good agreement is obtained between our approach and the expensive finite difference approximations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it