Electromagnetic scattering by numerical methods applicable for large structures
Bibliographic record
Abstract
The objective of this research is to develop numerical methods for general and efficient solutions to the linear systems obtained using the integral equations arising from electromagnetic scattering problems involving electrically large structures. In the process, the prior art in this area is reviewed. Then, the integral equations and their solutions by the method of moments (MoM) are derived. The progressive numerical method (PNM) and the projection iterative method (PIM) are analysed, including formulations, operation counts, stopping criteria, and their connection. In practice, the PNM is successful in calculation of two-dimensional scattering problems. The iterative PNM and a special case of the PNM, the modified spatial decomposition technique (SDT), are applied to the problems and compared with the PNM. Examples show that the PNM can depress internal resonances. The PIM is implemented in the two-dimensional TE case and convergent solutions are obtained. In order to overcome the difficulties with three-dimensional scattering problems, the PIM is implemented to solve the matrix equation obtained by MoM. Convergent results are observed in all examples being calculated for two- and three-dimensional objects. The PIM's iteration process can be accelerated by appropriate relaxation factors. The dependence of optimum relaxation factors on various parameters are investigated. Approximate results of large objects are obtained by the PIM with much less computation effort than the direct method. By allowing certain smaller elements in a coefficient matrix to be zero, the PIM can be further sped up, while still getting good far field results. This technique was found to be object dependent, providing better results for spheres than other objects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".