Efficient Electromagnetic Scattering Computation Using the Random Auxiliary Sources Method for Multiple Composite 3-D Arbitrary Objects
Bibliographic record
Abstract
The electromagnetic (EM) scattering problem from three-dimensional (3-D) arbitrary composite objects is proposed using the random auxiliary sources (RAS) method. Based on direct application of the boundary conditions with the uniqueness theorem and the use of random equivalent problems concept, more degrees-of-freedom to the sources' positions are added resulting in significantly efficient solutions with lower memory requirements. The technique does not require any singularity treatment due to placing the equivalent sources away from the boundaries. While boundary conditions are not enforced exactly, an iterative framework is introduced that can achieve an acceptable level of error in their satisfaction for an arbitrary, randomly generated set of equivalent sources. The presented technique promises a significant reduction in the execution time and memory requirements compared to the surface-equivalent-based method of moments (MoM). The solution stability, repeatability, and numerical noise susceptibility are investigated thoroughly through this work. Also, a novel edge correction scheme has been implemented to extend the capabilities of this procedure to structures with sharp edges. The results of the presented technique are compared to series solutions for conducting spheres and a commercially available MoM code for arbitrarily shaped objects and combinations of different materials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".