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Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Network
Therefore, the model can greatly alleviate the heavy burden of re-training since only the linear combination coefficients need to be found when changing the transmission condition.
Fiber Transmission Model with Parameterized Inputs based on GPT-PINN Neural Network
By taking into the account of the previously proposed principle driven fiber model, the reduced basis expansion method and transforming the parameterized inputs into parameterized coefficients of the Nonlinear Schrodinger Equations, universal solutions with respect to inputs corresponding to different bit rates can all be obtained without the need of re-training the whole model.
Fiber neural networks for the intelligent optical fiber communications
Though the potential ability of optical fiber was demonstrated via the establishing of fiber neural networks, it will be of great significance of combining both fiber transmission and computing functions so as to cater the needs of future beyond 5G intelligent communication signal processing.
Principle-driven Fiber Transmission Model based on PINN Neural Network
Instead of adopting input signals and output signals which are calculated by SSFM algorithm in advance before training, this principle-driven PINN based fiber model adopts frames of time and distance as its inputs and the corresponding real and imaginary parts of NLSE solutions as its outputs.
Electro-optical Neural Networks based on Time-stretch Method
In this paper, a novel architecture of electro-optical neural networks based on the time-stretch method is proposed and numerically simulated.
