Electromagnetic Sensitivity Analysis of Scattering Parameters Based on the FDFD Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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