Optimal Design of Large Mode Area Photonic Crystal Fibers Using a Multiobjective Gray Wolf Optimization Technique
Bibliographic record
Abstract
A new multiobjective optimization framework is presented for designing large mode area photonic crystal fibers (LMA-PCFs) with effective single-mode operation in the bent state. For optimizing the structure, we utilize the multiobjective gray wolf optimizer (MOGWO) to maximize the effective mode area (EMA) and the bending loss of higher order modes (HOMs), while minimizing the fundamental mode (FM) loss. The simulation results demonstrate that this framework enables us to improve the EMA by a factor of 1.26 and increase (decrease) the bending loss of the HOMs (the FM) by a factor of 7 (17), compared to the nonoptimal design. In addition, we investigate the dependence of the optical characteristics of the optimized LMA-PCFs on the wavelength and the bending radius. We found that some optimal structures are highly wavelength dependent and are not suitable for wideband applications. Furthermore, we found that the HOM loss is very sensitive to the bending radius. The proposed framework is comprehensive and can be employed to find a broad range of optimal designs for a wide range of applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".