H-Matrix Fast Direct Solution of Surface-Volume-Surface EFIE for Scattering Problems on General Composite Metal-Dielectric Objects
Bibliographic record
Abstract
Solution of radiation and scattering problems on metal-dielectric composite objects plays an important role in remote sensing, antenna design, and various other areas. We recently developed formulation of the Surface-Volume-Surface Electric Field Integral Equation (SVS-EFIE) [1] to solution of composite dielectric scatterers [2]. In this work we generalize the SVS-EFIE formulation to the case of piece-wise homogeneous scatterers which feature both penetrable dielectric regions and impenetrable metal regions. Independent electric surface current density is introduced on the boundary of each region forming the composite object. In addition, each common boundary between distinct regions of the scatterer features two independent unknown surface currents. This offers two advantages when it comes to numerical solution of the SVS-EFIE in comparison to the numerical solution of the classical surface integral equation formulations such as PMCHWT, Muller, or others. First, this independence of the unknown currents eliminates the problem of the current discretization at the material junctions. Second, the boundaries of the regions can be meshed independently and in correspondence with the material properties of their respective region which makes the proposed method more efficient for multiscale problems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".