High-Accurate Non-Uniform Grids for System-Combined ADI-FDTD Method in Near-Field Scattering With Proper CFL Factor
Bibliographic record
Abstract
In this paper, a high-accurate technique with non-uniform grids is introduced into a system-combined alternative-direction-implicit finite-difference time-domain (SC-ADI-FDTD) algorithm, and then successfully used to analyze electromagnetic propagations. To our knowledge, the conventional FDTD with non-uniform grids can be effectively deal with some edges of the three-dimensional cubes and complicated structures of the tiny objects by modulating the local grid scales, which to the extent improves its reliability and accuracy. However, due to existing the finer grids in the local computational region and the inevitable Courant-Friedrichs-Lewy (CFL) limit in the conventional FDTD, the temporal interval must be determined by the minimum fine spatial grid, resulting in much larger temporal sampling density required during the whole computation process. As the advantage of circumventing the repeated variables, the non-uniform SC-ADI-FDTD (NUSC-ADI-FDTD) cannot only break through the CFL limit to implement the high-efficient computation, but also further save more CPU time in the local microstructure cases. Furthermore, the empirical formula between the spatial sampling density and the CFL factors can be obtained from the numerical fitting method after errors analysis. The numerical simulations of the electromagnetic scattering have been executed to illustrate feasibility and validity of our proposed method.
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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) | 0.002 | 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".