Bibliographic record
Abstract
In the finite-difference time-domain (FDTD) method, the spatial step is usually chosen to be between 5% and 12.5% of the minimal wavelength of interest. If the boundaries cannot be positioned at integer multiples of the chosen spatial step, one usually reduces the spatial step or uses a nonuniform grid. Reported methods for treating such offset metal boundaries require substantial changes in the FDTD code, as well as changes in the grid and the time step. Recently, we proposed an alternative method (Rickard, Y.S. and Nikolova, N.K., 2004), whereby an off-grid perfect electric conductor (PEC) and magnetic boundaries may be modeled without disturbing the original grid. The method employs extrapolation of adjacent field values from the internal computational domain to obtain exterior field values ensuring off-grid virtual boundaries in-between. As only the outer boundary values are modified, the conventional FDTD code and the grid remain unchanged. Time step reduction is unnecessary, thus the high computation speed is preserved. To validate the method, we compare the calculated frequencies of the first few resonant modes of a rectangular resonator to their analytical values when the resonator has off-grid wall(s) in parallel with and slanted to the existing grid. We investigate the accuracy the stability with off-grid BCs and give recommendations for their use.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".