Inverse design of broadband and dispersion-flattened highly GeO2-doped optical fibers based on neural networks and particle swarm algorithm
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Abstract:
Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network combined with a particle swarm optimization algorithm. Firstly, the neural network model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters, achieving a root mean square error (RMSE) of 1.15?10-6 on the test dataset, with a determination coefficient (R-squared) of 0.999. Secondly, the neural network is combined with the particle swarm optimization algorithm for the inverse design of dispersion-flattened optical fibers. To expand the search space and avoid particles getting trapped in local optimal solutions, the particle swarm optimization algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm. Finally, by using a proper fitness function, the designed fibers exhibit flat group velocity dispersion (GVD) profiles at 1400-2400 nm, where the GVD fluctuations and minimum absolute GVD values are below 18 and 7 ps/nm?km, respectively.
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Project Supported:
the National Natural Science Foundation of China (No.61575018)
Clc Number:
