New concepts for the multiresolution time domain (MRTD) analysis of microwave structures
Bibliographic record
Abstract
nique presents a natural framework for the implementation of spatio-temporal adaptive gridding. This feature can lead to significant reductions in the simulation time for large-scale problems of practical interest to the microwave community. With the exception of the Haar-based MRTD scheme, all the other methods presented in the literature employ high-order finite difference operators for the numerical approximation of the spatial partial derivatives of Maxwell’s equations. However, little attention has been devoted to the study of the convergence properties of these schemes, which are typically associated with significantly increased numerical work and stability limits that are small fractions of the FDTD Courant stability limit. In this paper, it is first noted that high-order spatial finite differences still produce second-order error convergence for the method, as long as they are coupled with the second-order accurate leap-frog time integration. This prompts us to investigate other possibilities for the formulation of MRTD schemes, revisiting the choice of the leap-frog scheme for time-integration, with the purpose of improving the convergence properties of MRTD, employing high-order time integrators. I.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".