Phase computation for the finite-genus solutions to the focusing nonlinear Schrödinger equation using convolutional neural networks

Abstract

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.

Publication DOI: https://doi.org/10.1016/j.cnsns.2023.107311
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
Aston University (General)
Funding Information: SB, JEP, and SKT acknowledge the support from Leverhulme Trust, Grant No. RP-2018-063. PJF is supported by the EU Horizon 2020 program under the Marie Sklodowska-Curie grant agreement 813144 (REALNET). ES and SKT acknowledge the support of the EPSRC proje
Additional Information: Copyright © 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/).
Uncontrolled Keywords: nonlinear Schrödinger equation,Finite-genus solutions,Nonlinear Fourier transform,Riemann–Hilbert problem,Convolutional neural networks
Publication ISSN: 1007-5704
Last Modified: 09 Dec 2024 09:02
Date Deposited: 27 Jun 2023 14:59
Full Text Link:
Related URLs: https://www.sci ... 007570423002290 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-10
Published Online Date: 2023-05-22
Accepted Date: 2023-05-16
Authors: Bogdanov, Stepan
Shepelsky, Dmitry
Vasylchenkova, Anastasiia (ORCID Profile 0000-0002-6997-9427)
Sedov, Egor
Freire, Pedro J.
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)
Prilepsky, Jaroslaw E. (ORCID Profile 0000-0002-3035-4112)

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