We develop a method for retrieving a set of parameters of a quasi-periodic finite-genus (finite-gap) solution to the focusing nonlinear Schrödinger (NLS) equation, given the solution evaluated on a finite spatial interval for a fixed time. These parameters (named “phases”) enter the jump matrices in the Riemann-Hilbert (RH) problem representation of finite-genus solutions. First, we detail the existing theory for retrieving the phases for periodic finite-genus solutions. Then, we introduce our method applicable to the quasi-periodic solutions. The method is based on utilizing convolutional neural networks optimized by means of the Bayesian optimization technique to identify the best set of network hyperparameters. To train the neural network, we use the discrete datasets obtained in an inverse manner: for a fixed main spectrum (the endpoints of arcs constituting the contour for the associated RH problem) and a random set of modal phases, we generate the corresponding discretized profile in space via the solution of the RH problem, and these resulting pairs – the phase set and the corresponding discretized solution in a finite interval of space domain – are then employed in training. The method’s functionality is then verified on an independent dataset, demonstrating our method’s satisfactory performance and generalization ability.
|Number of pages
|Communications in Nonlinear Science and Numerical Simulation
|Early online date
|22 May 2023
|Published - Oct 2023
Bibliographical noteCopyright © 2023, The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
- nonlinear Schrödinger equation
- Finite-genus solutions
- Nonlinear Fourier transform
- Riemann–Hilbert problem
- Convolutional neural networks