In this work, we propose to utilize the artificial neural network (ANN) to realize the computing of the pulse performance of the linear cavity fiber laser. At the first, a four hidden layer ANN (called ANN1) is trained to judge whether a small noise pulse in the fiber cavity can evolve into a stable mode-locked pulse with different cavity parameters. ANN1 has an accuracy of 98.3% on the test data set and we use it to quickly calculate the pulse convergence region in the three-dimensional parameter space. Then, a three hidden layer ANN (called ANN2) is trained to calculate the output pulses shape of fiber laser, and its accuracy is verified. After that, based on ANN2 and genetic algorithm, we design a method to inverse deducing the laser parameters with known output pulse width. This algorithm has a small-time complexity. By repeating the genetic process, the accuracy of this algorithm will also be improved. The authors believe that the neural network model presented in this work is an efficient and universal means to study the dynamics of optical fibers and will have a great application prospect in future related work.
We use the full vector beams propagation method (VFE-BPM) to simulate the nonlinear propagation process of the cylindrical vector beams (CVBs). We have shown that the CVBs have poor transverse stability which causes which causes the breakup due to the ring-shaped beam field. Then, we use the approximate analytic theory to qualitative analyze the variations of the numerically calculated instability growth rates, which shows excellent agreement with the simulated results.
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