Trapping dynamics in nonlinear wave scattering by local guiding defects
Bibliographic record
Abstract
We study numerically the trapping dynamics of nonlinear waves scattered by local guiding photonic centers with normal eigenmodes which are embedded in uniform nonlinear Kerr waveguides. The linear and nonlinear scattering from a local defect may be treated from either a wave optics approach or a ray optics approach. The former provides a better understanding of the wave dynamics while the latter enables one to perform quasi-analytical estimates of the extent of trapping in a given structure. In the presence of a single-site multi-mode local scattering center, power may localize in a certain normal mode of the center, or periodically oscillate between different normal modes. The degree of trapping and mode of localization can be controlled as function of both the input power and the angle of incidence. With multi-site local scattering centers, the trapping dynamics are strongly dependent on the degree of coupling between the neighboring sites. In the scattering by a local multi-site defect with strongly coupled adjacent sites, two possibilities for nonlinear trapping arise. At intermediate nonlinear powers, periodic tunneling of power between adjacent sites and their normal modes is observed. At highly nonlinear powers, but still within experimental feasibility, the radiation can become strongly localized in a single site. The scattering and trapping dynamics are also described in the context of nonlinear Fabry-Perot etalons, as a function of the local defect's refractive index.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".