A domain decomposition method for waveguide horn problems using the finite element method
Bibliographic record
Abstract
A Domain Decomposition Method (DDM) for solving electromagnetic radiation problems using the finite element method is proposed. To minimize the size of the computational domain, disjoint scatterers and/or radiators which are relatively far from each others are bounded separately. An iterative Absorbing Boundary Condition (ABC) is applied upon an outer boundary of arbitrary shape which may be conformal to the surface of each scatterer and radiator. The boundary condition of each subdomain is updated iteratively using the contribution of the scattered field from the subdomain itself and also from the other subdomains, which makes it fully coupled. An accurate waveguide port boundary condition is employed to model horn antenna feeds. Unlike most DDMs, the proposed method uses an arbitrary shaped truncation boundary placed very close to the scatterers and radiators of each disjoint subdomain to minimize the computational domain; and more importantly, the method produces a solution that converges to the true solution (if we ignore discretization error) as the iteration proceeds. A numerical example is provided to demonstrate the correctness of the feed modelling by computing antenna input impedance. Results are provided to validate the multi-domain approach and compare it with the single domain results.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".