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

Yassin Boussafa, Lynn Sader, Hoang Van Thuy, Bruno P. Chaves, Alexis Bougaud, Marc Fabert, Alessandro Tonello, John M. Dudley, Michael Kues and Benjamin Wetzel ()
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Yassin Boussafa: University of Limoges
Lynn Sader: University of Limoges
Hoang Van Thuy: University of Limoges
Bruno P. Chaves: University of Limoges
Alexis Bougaud: University of Limoges
Marc Fabert: University of Limoges
Alessandro Tonello: University of Limoges
John M. Dudley: CNRS Institut FEMTO-ST
Michael Kues: Leibniz University Hannover
Benjamin Wetzel: University of Limoges

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-62713-x

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