Affiliation:
1. State Key Lab of Advanced Communication Systems and Networks School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China
2. Ultrafast Laser Laboratory Key Laboratory of Opto‐electronic Information Science and Technology of Ministry of Education College of Precision Instruments and Opto‐electronics Engineering Tianjin University Tianjin 300072 China
Abstract
AbstractAs an imperative method of investigating the internal mechanism of femtosecond lasers, traditional femtosecond laser modeling relies on the split‐step Fourier method (SSFM) to iteratively resolve the nonlinear Schrödinger equation suffering from the large computation complexity. To realize inverse design and optimization of femtosecond lasers, numerous simulations of mode‐locked fiber lasers with different cavity settings are required, further highlighting the time‐consuming problem induced by the large computation complexity. Here, a recurrent neural network is proposed to realize fast and accurate femtosecond mode‐locked fiber laser modeling. The generalization over different cavity settings is achieved via the proposed prior information feeding method. With the acceleration of GPU, the mean time of the artificial intelligence (AI) model inferring 500 roundtrips is less than 0.1 s, which is ≈146 times faster than the SSFM running on a CPU. The proposed AI‐enabled method is promising to become a standard approach to femtosecond laser modeling.
Funder
National Natural Science Foundation of China
Subject
Condensed Matter Physics,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Cited by
13 articles.
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