Comparison of TE and TM Inversions in the Framework of the Gauss-Newton Method
Bibliographic record
Abstract
The Gauss-Newton inversion method in conjunction with a regularized formulation of the inverse scattering problem is used to invert transverse electric (TE) and transverse magnetic (TM) data. The utilized data sets consist of experimental data provided by the Institut Fresnel as well as synthetic data. The TE inversion outperformed the TM inversion when utilizing near-field scattering data collected using only a few transmitters and receivers. However, very little difference was found between TE and TM inversions when using far-field scattering data. It is conjectured that the reason for the better performance of the near-field TE result is that the near-field TE data contains more information than the near-field TM data at each receiver point. In all cases considered herein, the TE inversion required equal or fewer iterations than the TM inversion. The per-iteration computational complexity of both TE and TM inversions is discussed in the framework of the Gauss-Newton inversion method. Actual costs are consistent with the computational complexity analysis that is given.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".