Neural networks for computing and denoising the continuous nonlinear Fourier spectrum in focusing nonlinear Schrodinger equation

We combine the nonlinear Fourier transform (NFT) signal processing with machine learning methods for solving the direct spectral problem associated with the nonlinear Schrodinger equation. The latter is one of the core nonlinear science models emerging in a range of applications. Our focus is on the unexplored problem of computing the continuous nonlinear Fourier spectrum associated with decaying profiles, using a specially-structured deep neural network which we coined NFT-Net. The Bayesian optimisation is utilised to find the optimal neural network architecture. The benefits of using the NFT-Net as compared to the conventional numerical NFT methods becomes evident when we deal with noise-corrupted signals, where the neural networks-based processing results in effective noise suppression. This advantage becomes more pronounced when the noise level is sufficiently high, and we train the neural network on the noise-corrupted field profiles. The maximum restoration quality corresponds to the case where the signal-to-noise ratio of the training data coincides with that of the validation signals. Finally, we also demonstrate that the NFT b-coefficient important for optical communication applications can be recovered with high accuracy and denoised by the neural network with the same architecture.

Automated summary

The research combines nonlinear Fourier transform with machine learning methods to solve the direct spectral problem associated with the nonlinear Schrodinger equation, focusing on computing the continuous nonlinear Fourier spectrum for decaying profiles. Using the NFT-Net neural network proves beneficial for effective noise suppression, especially in dealing with high levels of noise.