Efficient electromagnetic optimization using self-adjoint Jacobian computation based on a central-node FDFD method
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Bibliographic record
Abstract
We propose a sensitivity solver for frequency-domain analysis engines based on volume methods such as the finite-element method. Our sensitivity solver computes S-parameter Jacobians directly from the field solution available from the electromagnetic simulation. The computational overhead is a fraction of that of the simulation itself. It is independent from the simulator’s grid, system equations and discretization method. It uses its own finite-difference grid and a sensitivity formula based on the frequency-domain finite-difference (FDFD) equation for the electric field. It computes the S-parameter gradients in the design parameter space through a self-adjoint formulation which eliminates adjoint system analyses and greatly simplifies implementation. We use our sensitivity solver in gradient-based optimization of filters. We achieve drastic reduction of the time required by the overall optimization process. All examples use a commercial finite-element simulator.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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