Modeling of the perfect electromagnetic conducting boundary in the finite difference time domain method
Bibliographic record
Abstract
Abstract The perfect electromagnetic conducting (PEMC) boundary, a nonreciprocal generalization of both perfect electric conducting (PEC) and perfect magnetic conducting (PMC) boundaries, is modeled in the finite difference time domain (FDTD) method. Since the PEMC boundary condition requires collocation of same components of both electric and magnetic fields at the boundary grids, which is not compatible with the original FDTD algorithm, its implementation in FDTD is challenging and requires modification in the algorithm. To do this task, first, the original FDTD cell is modified by inserting the required field components not present in the original cell. Then, a novel formulation is developed for updating fields' components at the boundary. Modeling of a PEMC planar interface, a corner point, and a wedge point are presented. Finally, numerical examples are presented to show stability, accuracy, and applicability of the proposed approach. Validation is achieved by comparisons with existing analytic methods and/or conventional FDTD for special cases of PEC and PMC boundaries.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".