A Macromodeling Approach to Efficiently Compute Scattering from Large Arrays of Complex Scatterers
Bibliographic record
Abstract
Full-wave electromagnetic simulations of electrically large arrays of complex antennas and scatterers are challenging, as they consume a large amount of memory and require long CPU times. This paper presents a new reduced-order modeling technique to compute scattering and radiation from large arrays of complex scatterers and antennas. In the proposed technique, each element of the array is replaced by an equivalent electric current distribution on a fictitious closed surface enclosing the element. This equivalent electric current density is derived using the equivalence theorem, and it is related to the surface currents on the scatterer by the Stratton-Chu formulation. With the proposed approach, instead of directly solving for the unknown surface current density on the scatterers, we only need to solve for the unknowns on the equivalent surface. This approach leads to a reduction in the number of unknowns and better conditioning when it is applied to problems involving complex scatterers with multiscale features. Furthermore, the proposed approach is accelerated with the adaptive integral equation method to solve large problems. As illustrated in several practical examples, the proposed method yields the speedup of up to 20 times and consumes up to 12 times less memory than the standard method of moments accelerated with the adaptive integral method.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".