Memory-Efficient Method for Wideband Self-Adjoint Sensitivity Analysis
Bibliographic record
Abstract
Sensitivity analysis is crucial in microwave imaging and design procedures. We proposed a time-domain self- adjoint method for the computation of response Jacobians. The responses and their derivatives are computed with a single time-domain analysis. The overhead of the Jacobian computation is negligible compared to the time required by the simulation even when the number of optimizable parameters exceeds thousands. However, two drawbacks have become obvious: 1) memory requirements may become excessive when the number of perturbation grid points is large and the simulation time is long and 2) Jacobian accuracy may degrade due to the intrinsic inaccuracy of the local numerical field solution at dielectric interfaces of high contrast. Here, we propose an improved method for the self-adjoint computation of the Jacobian. It drastically reduces the memory requirements by implementing a novel spectral sensitivity formula, which operates on the spectral components of the E-field rather than on its time waveforms. It significantly improves the accuracy of the Jacobian by departing from the conventional finite-difference Yee cell and employing its own independent central-node finite-difference grid. The proposed approach is validated by 2-D and 3-D examples with lossy dielectric inhomogeneous structures. This study aims at the acceleration of wideband microwave image reconstruction via efficient Jacobian calculation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".