Mehdi MABED : "Application of deep learning methods to the analysis and modeling of nonlinear dynamics in optical fibers"

Abstract : Nonlinear pulse propagation in optical fibers exhibits a wide range of complex dynamics. Depending on the initial conditions and parametres of the fiber used, it is possible to observe a range of instability and solitonic dynamics leading to pronounced spectral broadening, and even supercontinuum generation. The complexity of these effects can make their analysis and modeling difficult, and the aim of this thesis has been to demonstrate the application of machine learning techniques to analyse different scenarios of nonlinear pulse propagation governed by the nonlinear Schrödinger equation. In particular, the prediction of the temporal properties of optical "rogue waves" based solely on spectral intensity analysis, without any phase information, was accomplished using a feedforward neural network. These predictions proved highly satisfactory in systems of pure modulation instability, as well as noise-like pulse laser operation in narrow- and wide-band spectral regimes. Finally, the feedforward neural network algorithm also proved remarkably accurate in reconstructing the dynamics of the nonlinear Schrödinger equation.

Jury Composition :

John DUDLEY Professeur Université Bourgogne Franche-Comté PhD Director

Ariel LEVENSON Directeur de recherche C2N-Université Paris-Saclay Reporter

Arnaud MUSSOT Professeur Université de Lille Reporter

Bertrand KIBLER Directeur de recherche Université Bourgogne Franche-Comté Reviewer

Benjamin WETZEL Chargé de recherche Université de Limoges Reviewer

Localisation : 15B, avenue des Montboucons 25000 BESANCON - salle Amphithéâtre Jean-Jacques Gagnepain