Investigation of projection iterative method in solving MoM matrix equations in electromagnetic scattering
Bibliographic record
Abstract
The projection iterative method (PIM) is convergence guaranteed when applied to solve the MoM equations with nonsingular matrices. Its decomposition procedure divides the matrix into some small subregions to avoid large matrix inversions. It is found that the convergent rate can be accelerated by introducing the relaxation factor to the PIM formulation. Three 3D examples are investigated to show the performance and validation of the PIM on electromagnetic scattering problems. A 2D infinite circular cylinder with The field illumination is also studied to show the convergence of the method. The relationship of various PIM related parameters, such as the normalised residual norm, the number of iterations, the number of divided subregions, and the relaxation factor, is studied and presented. It is concluded that the operation count of the accelerated PIM is usually comparable to the direct method and the PIM can predict the RCS faster than the direct method with a reasonable accuracy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".