A subgridding scheme using hybrid one-step leapfrog ADI-FDTD and FDTD methods
Why this work is in the frame
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Bibliographic record
Abstract
A novel subgridding scheme that hybridizes the recently developed unconditionally stable one-step leapfrog alternately-direction-implicit finite-difference time-domain (ADI-FDTD) method and the conventional finite-difference time-domain (FDTD) method is proposed. The conventional explicit FDTD method is applied to coarse mesh regions while the leapfrog ADI-FDTD method to locally subgridded mesh regions. The difference between the proposed subgridding scheme and the existing ones lie in the fact that only spatial interpolation of fields is required at the interface between coarse and subgridded meshes. As a result, computational efficiency is improved while numerical stability maintained. Both stability and efficiency are verified through numerical experiments in simulating a substrate integrated waveguide.
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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 | 0.001 | 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) | 0.001 | 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 it