Multi‐region ADI DD‐FDTD algorithm for the analysis of three‐dimensional sparse multi‐objects scattering problem
Bibliographic record
Abstract
Abstract In this paper, a multi‐region domain decomposition finite‐difference time‐domain (DD‐FDTD) algorithm is proposed and developed for the analysis of multiple‐objects electromagnetic (EM) problems. A significant number of mesh nodes between objects are removed since only local meshes are generated for each object. All the separated sub‐domains are interconnected by the use of a 3‐D time‐domain Green's function. The coupling between objects can be regarded as the equivalent spherical wave irradiations. Incident signals of the equivalent spherical waves are expressed as a spherical wave input field array according to the Huygens principle. The near‐field to far‐field transformation is introduced to obtain the equivalent spherical wave. Moreover, the alternating direction implicit FDTD (ADI‐FDTD) scheme is applied to overcome the limit of the stability condition and increase the speed of the simulation. The new algorithm has been demonstrated and applied to solve typical 3‐D multi‐objects EM scattering problems. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".