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Suppressing modulation instability with reinforcement learning

Author

Listed:
  • Kalmykov, N.I.
  • Zagidullin, R.
  • Rogov, O.Y.
  • Rykovanov, S.
  • Dylov, D.V.

Abstract

Modulation instability is a phenomenon of spontaneous pattern formation in nonlinear media, oftentimes leading to an unpredictable behavior and a degradation of a signal of interest. We propose an approach based on reinforcement learning to suppress the unstable modes in the system by optimizing the parameters for the time modulation of the potential in the nonlinear system. We test our approach in 1D and 2D cases and propose a new class of physically-meaningful reward functions to guarantee tamed instability.

Suggested Citation

  • Kalmykov, N.I. & Zagidullin, R. & Rogov, O.Y. & Rykovanov, S. & Dylov, D.V., 2024. "Suppressing modulation instability with reinforcement learning," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:chsofr:v:186:y:2024:i:c:s0960077924007495
    DOI: 10.1016/j.chaos.2024.115197
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    References listed on IDEAS

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    1. Bethany Lusch & J. Nathan Kutz & Steven L. Brunton, 2018. "Deep learning for universal linear embeddings of nonlinear dynamics," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
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