Machine learning aided inverse design for flattop beam fiber

The flattop (FT) beam, one important laser beam, is often applied to the high-power fiber laser. It is preferred to generate the FT beam by M-type fiber. However, the design of optical fiber structure is complex and time-consuming. In this work, based on the M-type fiber, a machine learning method using artificial neural network (ANN) is proposed to inversely design the FT beam fiber. By using this trained ANN, the inverse design of the FT beam fiber is realized, according to the performances of FT beam, the structural parameters of M-type fiber are determined. In addition, the influence of structural parameters on the performances of FT beam, including the flatness, the power confining factor and the effective area, are discussed in detail. The proposed ANN-based machine learning method provides an efficient, accurate prediction for FT beam fiber with excellent performances.

Automated summary

In this study, an artificial neural network-based machine learning method is proposed for the inverse design of flattop (FT) beam fiber. The trained network accurately predicts the structural parameters and performance of FT beam, providing efficient and accurate results.