Deep learning prediction of noise-driven nonlinear instabilities in fibre optics

authored by: Yassin Boussafa, Lynn Sader, Van Thuy Hoang, Bruno P. Chaves, Alexis Bougaud, Marc Fabert, Alessandro Tonello, John M. Dudley, Michael Kues, Benjamin Wetzel
Abstract: Machine learning is bringing revolutionary approaches into many fields of physics. Among those, photonics enables fast and scalable information processing. Photonics platforms further possess rich nonlinear dynamics that drive fundamental interest but also prove powerful for applications in computation, imaging, frequency conversion, source development and advanced signal processing. However, incoherent processes of nonlinear optics are hardly exploited in practice as the control of noise-driven dynamics remains challenging. Here, we exploit deep learning strategies and demonstrate that coherent optical seeding can effectively shape incoherent spectral broadening. We focus on the intricate interplay between weak coherent pulses and broadband noise, competing during nonlinear fibre propagation within an amplification process known as modulation instability. We demonstrate artificial neural networks’ capability to efficiently predict these complex incoherent dynamics, both numerically and experimentally. Our results show that input seed properties can be inferred from the incoherent output signal. Furthermore, our approach enables reliable prediction of output spectral fluctuations, paving the way to tailoring complex photonic signals with specific correlation features.
Organisation(s): Institute of Photonics
PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines
External Organisation(s): Universite de Limoges
Institute FEMTO-ST
Institut Universitaire de France
Type: Article
Journal: Nature Communications
Volume: 16
ISSN: 2041-1723
Publication date: 21.08.2025
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: General Chemistry, General Biochemistry,Genetics and Molecular Biology, General, General Physics and Astronomy
Electronic version(s): https://doi.org/10.1038/s41467-025-62713-x (Access: Open)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{b7bb4979526046acafb29632880df81a,
title = "Deep learning prediction of noise-driven nonlinear instabilities in fibre optics",
abstract = "Machine learning is bringing revolutionary approaches into many fields of physics. Among those, photonics enables fast and scalable information processing. Photonics platforms further possess rich nonlinear dynamics that drive fundamental interest but also prove powerful for applications in computation, imaging, frequency conversion, source development and advanced signal processing. However, incoherent processes of nonlinear optics are hardly exploited in practice as the control of noise-driven dynamics remains challenging. Here, we exploit deep learning strategies and demonstrate that coherent optical seeding can effectively shape incoherent spectral broadening. We focus on the intricate interplay between weak coherent pulses and broadband noise, competing during nonlinear fibre propagation within an amplification process known as modulation instability. We demonstrate artificial neural networks{\textquoteright} capability to efficiently predict these complex incoherent dynamics, both numerically and experimentally. Our results show that input seed properties can be inferred from the incoherent output signal. Furthermore, our approach enables reliable prediction of output spectral fluctuations, paving the way to tailoring complex photonic signals with specific correlation features.",
author = "Yassin Boussafa and Lynn Sader and Hoang, {Van Thuy} and Chaves, {Bruno P.} and Alexis Bougaud and Marc Fabert and Alessandro Tonello and Dudley, {John M.} and Michael Kues and Benjamin Wetzel",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2025.",
year = "2025",
month = aug,
day = "21",
doi = "10.1038/s41467-025-62713-x",
language = "English",
volume = "16",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",
}

Back