Point-based discretization of the mixed-potential RWG MoM via the Nystrom method
Bibliographic record
Abstract
Summary form only given. Enforcing continuity of the approximated field between elements of the mesh can improve accuracy of a numerical solution as well as conditioning of its impedance matrix (M. Shafieipour, et. al., IEEE Trans. Antennas Propag., 99, 1-11, 2013). In the class of low-order solutions, Rao-Wilton-Glisson (RWG) basis functions are widely used in the numerical solution of electromagnetic scattering problems and they have been recently suggested in conjunction with first-order Locally Corrected Nystrom (LCN) method when solution of the Electric Field Integral Equation (EFIE) is perused (M. Shafieipour, et. al., IEEE Trans. Antennas Propag., 99, 1-11, 2013), resulting in a current-continuity-enforcing point-based discretization scheme (RWG-via-LCN) which can efficiently be accelerated by the Multilevel Fast Multipole Algorithm (MLFMA). However, it is known that the LCN method suffers from low-frequency breakdown (J. C. Young, et. al., IEEE Antennas. Wireless. Propag. Lett., 11, 846-849, 2012) when trying to solve for the EFIE as LCN discretizes the vector-potential EFIE as opposed to the mixed-potential EFIE. In this work we show that the RWG-via-LCN method inherits the low-frequency breakdown from the LCN method. As a remedy, we introduce a new RWGvia-LCN scheme which is a mixture of zerothand first-order discretization of the EFIE and we show that it is equivalent to the mixed-potential RWG MoM. The new method preserves all advantages of the RWG-via-LCN method (i.e. point-based and current-continuity-enforcing) and at the same time enjoys a wide-band solution and is computationally more efficient as it uses zeroth-order EFIE to compute the scalar potential contribution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.006 | 0.002 |
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".