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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".