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18 lut 2021 · Here, we present a solution to this problem using a recurrent neural network to model and predict complex nonlinear propagation in optical fibre, solely from the input pulse intensity profile.
2 lip 2024 · This paper reviews developments in the fabrication and post-processing of such semiconductor core fibres and their enabling of low loss and high efficiency nonlinear components across...
15 paź 2020 · Ultrafast Tm-doped fibre lasers have been actively studied for the last decade due to their potential applications in precise mid-IR spectroscopy, LIDARs, material processing and more.
In this paper, a novel deep learning architecture, Fourier neural operator (FNO), has been introduced to ap-proximate the nonlinear Schrodinger equation which characterizes fiber transmission impairments such as fiber attenuation, chromatic dispersion, nonlinear impairments, etc.
26 lip 2024 · Here, we construct an inverse parameterized Manakov model based on the neural operator, utilizing it to compensate the fiber nonlinearity impairments at low computational complexity. The model proposed has been validated in a 12,075 km wavelength division multiplexing (WDM) system.
In this paper, a low-complexity convolutional recurrent neural network (CNN + RNN) is considered for deep learning of the long-haul optical fiber communication systems where the channel is governed by the nonlinear Schrodinger equation.
1 mar 2023 · Accurate and reliable modeling techniques are required to properly understand and predict manufacturing processes and the quality of the final product. The Automated Fiber Placement (AFP) process in its entirety is commonly difficult to predict due to the complex and interconnected phases of the manufacturing lifecycle.
