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Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network

Nature Machine Intelligence volume 3, pages 387–391 (2021)Cite this article

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The Original Article was published on 18 February 2021

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arising from L. Salmela et al. Nature Machine Intelligence https://www.nature.com/articles/s42256-021-00297-z (2021)

With their internal memory, recurrent neural networks can be used to learn and predict time-dependent behaviours. In their recent work, Salmela et al.1 present a recurrent neural network architecture to learn and predict complex nonlinear propagation in an optical fibre based on the input pulse intensity profile in the time domain. Here, we use their model by extending it to the case of spatiotemporal nonlinear propagation for an arbitrary number of modes in graded-index multimode fibres. In addition to the original work’s focus on predicting the temporal evolution of pulses, we show that the method is applicable for modelling and predicting spatial beam propagation incorporating nonlinear mode coupling.

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Fig. 1: An example of the spectral intensity evolution of a high-power femtosecond pulse in a graded-index multimode fibre.
Fig. 2: An example of temporal intensity evolution of a high-power femtosecond pulse in a graded-index multimode fibre.
Fig. 3: An example spatial intensity evolution of a 1-GW femtosecond pulse in a graded-index multimode fibre.

Data availability

The data used in this paper is available at the following GitHub repository https://github.com/ugurtegin/MMF_RNN_Reuse.

Code availability

The code used in this paper is available at the following GitHub repository https://github.com/ugurtegin/MMF_RNN_Reuse.

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Acknowledgements

We thank M. Yıldırım for discussions.

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Authors and Affiliations

  1. Optics Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Uğur Teğin, Niyazi Ulaş Dinç & Demetri Psaltis

  2. Laboratory of Applied Photonics Devices, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Uğur Teğin, Niyazi Ulaş Dinç & Christophe Moser

Authors
  1. Uğur Teğin
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  2. Niyazi Ulaş Dinç
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  3. Christophe Moser
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  4. Demetri Psaltis
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Contributions

U.T. and N.U.D. performed simulations; C.M and D.P. supervised and directed the project. All the authors participated in the analysis of the data and the writing process of the manuscript.

Corresponding authors

Correspondence to Uğur Teğin or Niyazi Ulaş Dinç.

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The authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks Yichen Wu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figures 1–6 and Supplementary Discussion

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Teğin, U., Dinç, N.U., Moser, C. et al. Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network. Nat Mach Intell 3, 387–391 (2021). https://doi.org/10.1038/s42256-021-00347-6

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