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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62713-x |
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| author | Yassin Boussafa Lynn Sader Van Thuy Hoang Bruno P. Chaves Alexis Bougaud Marc Fabert Alessandro Tonello John M. Dudley Michael Kues Benjamin Wetzel |
| author_facet | Yassin Boussafa Lynn Sader Van Thuy Hoang Bruno P. Chaves Alexis Bougaud Marc Fabert Alessandro Tonello John M. Dudley Michael Kues Benjamin Wetzel |
| author_sort | Yassin Boussafa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3c3cf48c9968400286d7ca7c7d8926d0 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-3c3cf48c9968400286d7ca7c7d8926d02025-08-24T11:38:09ZengNature PortfolioNature Communications2041-17232025-08-0116111410.1038/s41467-025-62713-xDeep learning prediction of noise-driven nonlinear instabilities in fibre opticsYassin Boussafa0Lynn Sader1Van Thuy Hoang2Bruno P. Chaves3Alexis Bougaud4Marc Fabert5Alessandro Tonello6John M. Dudley7Michael Kues8Benjamin Wetzel9XLIM Research Institute, CNRS UMR 7252, University of LimogesXLIM Research Institute, CNRS UMR 7252, University of LimogesXLIM Research Institute, CNRS UMR 7252, University of LimogesXLIM Research Institute, CNRS UMR 7252, University of LimogesXLIM Research Institute, CNRS UMR 7252, University of LimogesXLIM Research Institute, CNRS UMR 7252, University of LimogesXLIM Research Institute, CNRS UMR 7252, University of LimogesUniversité Marie et Louis Pasteur, CNRS Institut FEMTO-STInstitute of Photonics and Cluster of Excellence PhoenixD, Leibniz University HannoverXLIM Research Institute, CNRS UMR 7252, University of LimogesAbstract 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.https://doi.org/10.1038/s41467-025-62713-x |
| spellingShingle | Yassin Boussafa Lynn Sader Van Thuy Hoang Bruno P. Chaves Alexis Bougaud Marc Fabert Alessandro Tonello John M. Dudley Michael Kues Benjamin Wetzel Deep learning prediction of noise-driven nonlinear instabilities in fibre optics Nature Communications |
| title | Deep learning prediction of noise-driven nonlinear instabilities in fibre optics |
| title_full | Deep learning prediction of noise-driven nonlinear instabilities in fibre optics |
| title_fullStr | Deep learning prediction of noise-driven nonlinear instabilities in fibre optics |
| title_full_unstemmed | Deep learning prediction of noise-driven nonlinear instabilities in fibre optics |
| title_short | Deep learning prediction of noise-driven nonlinear instabilities in fibre optics |
| title_sort | deep learning prediction of noise driven nonlinear instabilities in fibre optics |
| url | https://doi.org/10.1038/s41467-025-62713-x |
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