Rigorous analysis of negative refractive index metamaterials using FDTD with embedded lumped elements
Bibliographic record
Abstract
A methodology for the time-domain analysis of negative refractive index (NRI) media is proposed in this paper. Based on circuit models of NRI meta-materials and associated planar implementations that have been recently demonstrated, an extended FDTD approach is formulated, combining Maxwell's equations with lumped element voltage-current characteristics. Compared to previous FDTD modelling of NRI materials as negative dispersive index media, the proposed method presents the significant advantage of relying on simple and well-known mesh truncation method and not suffering from instabilities related to the Lorentz model poles. The analysis is accelerated by invoking periodic boundary conditions that allow for the simulation of a single unit cell as opposed to the whole grid. As a time-domain technique, the proposed one allows for the clarification of the transients involved with the evolution of backward waves and negative refraction in NRI meta-materials and the verification of the fact that causality is preserved throughout. Numerical results include validation against finite element analysis and converge studies that indicate the efficiency and computational speed of this method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".