Inverse design of figure eight fiber laser by artificial neural network
Bibliographic record
Abstract
Fiber lasers have become indispensable tools in modern photonics, offering unparalleled efficiency, stability, and versatility. Among them, the figure-eight fiber laser (F8FL) has gained prominence for its ability to generate ultra-short pulses with high peak power, making it highly suitable for applications in ultrafast spectroscopy, nonlinear microscopy, and optical frequency comb generation. However, designing and optimizing F8FLs remains a significant challenge due to the intricate interplay of nonlinear effects, dispersion management, and gain dynamics. Traditional design approaches rely on numerical simulations and iterative experimental tuning, which are computationally expensive and often yield suboptimal results. To address these challenges, we introduce a machine learning-based inverse design framework for optimizing F8FL parameters. Using a dataset generated from numerical simulations, an artificial neural network (ANN) is trained to establish a direct mapping between pulse characteristics and the key amplifier parameters, including small-signal gain and saturation energy. This approach enables rapid and accurate prediction of laser settings required to achieve a target pulse profile, significantly reducing the computational burden compared to conventional numerical methods. Our results demonstrate that the trained ANN model achieves excellent agreement with numerical simulations, effectively predicting the optimal parameters for producing high-energy rectangular pulses in the dissipative soliton resonance (DSR) regime. To validate the effectiveness of the predicted parameters, the ANN outputs were independently verified using OptiSystem simulations, confirming strong agreement with the desired pulse profiles. This study highlights the potential of machine learning in photonics, paving the way for the development of self-optimizing, adaptive laser systems with enhanced precision and efficiency. The proposed methodology can be extended to other nonlinear optical systems, offering a powerful tool for accelerating the design and optimization of advanced fiber lasers.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".