A meshless based solution to vectorial mode fields in optical microstructured waveguides
Bibliographic record
Abstract
A meshless solution to vectorial mode fields has been applied to various micro-structured optical waveguides. The Finite Cloud Method (FCM), has been used to solve coupled field equations for both transverse components of the magnetic field as well as the effective index of refraction for the waveguides. Two methods using either a step-index or a graded-index have been implemented and compared. An approximation to the solution is found using a distribution of points and a cloud about each point, with no mesh and minimal geometric linking knowledge between the points. This gives the ability to use a highly irregular point distribution which can be easily modified or tailored to micro-structured fibers in order to accurately represent the vectorial modal solution. In addition, the use of Bayliss-Gunzburger-Turkel-like transparent boundary conditions (TBC) and an iterative process is compared with a perfectly matched layer (PML), both of which allow for the solution of leaky modes for the structures. Results for ridge waveguides and solid core fibers having low index contrast are in high agreement with the solutions from commercial solvers. Further results with high contrast air hole structures are compared with other solution methods giving promising results and highlight this methods versatility, accuracy and efficiency for a wide range of problems.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".