Controlling chaotic maps using next-generation reservoir computing

Kent, Robert M.; Barbosa, Wendson A. S.; Gauthier, Daniel J.

doi:10.1063/5.0165864

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Rather, we use a data-driven model learned during the training phase. Robust and stable control of the dynamical system is obtained 21 , 23 by taking where, is the desired state of the system m -steps-ahead in the future, is a closed loop gain matrix, and is the tracking error. The ″^″ over the symbols indicate that these quantities are learned during the training phase using the procedure described in the next subsection.…”

Section: Resultsmentioning

confidence: 99%

Controlling chaos using edge computing hardware

Kent,

Barbosa,

Gauthier

2024

Nat Commun

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Machine learning provides a data-driven approach for creating a digital twin of a system – a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often, the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedded computing hardware that can operate without a cloud-computing connection. Here, we show that a nonlinear controller based on next-generation reservoir computing can tackle a difficult control problem: controlling a chaotic system to an arbitrary time-dependent state. The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array typically found in embedded devices. Furthermore, the model only requires 25.0 $$\pm 7.0$$ ± 7.0 nJ per evaluation, well below other algorithms, even without systematic power optimization. Our work represents the first step in deploying efficient machine learning algorithms to the computing “edge.”

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Section: Resultsmentioning

confidence: 99%

Controlling chaos using edge computing hardware

Kent,

Barbosa,

Gauthier

2024

Nat Commun

Self Cite

View full text Add to dashboard Cite

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Chaos synchronization of two coupled map lattice systems using safe reinforcement learning

Ding,

Lei,

Xie

et al. 2024

Chaos, Solitons & Fractals

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Photonic next-generation reservoir computer based on distributed feedback in optical fiber

Cox,

Murray,

Hart

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Reservoir computing (RC) is a machine learning paradigm that excels at dynamical systems analysis. Photonic RCs, which perform implicit computation through optical interactions, have attracted increasing attention due to their potential for low latency predictions. However, most existing photonic RCs rely on a nonlinear physical cavity to implement system memory, limiting control over the memory structure and requiring long warm-up times to eliminate transients. In this work, we resolve these issues by demonstrating a photonic next-generation reservoir computer (NG-RC) using a fiber optic platform. Our photonic NG-RC eliminates the need for a cavity by generating feature vectors directly from nonlinear combinations of the input data with varying delays. Our approach uses Rayleigh backscattering to produce output feature vectors by an unconventional nonlinearity resulting from coherent, interferometric mixing followed by a quadratic readout. Performing linear optimization on these feature vectors, our photonic NG-RC demonstrates state-of-the-art performance for the observer (cross-prediction) task applied to the Rössler, Lorenz, and Kuramoto–Sivashinsky systems. In contrast to digital NG-RC implementations, we show that it is possible to scale to high-dimensional systems while maintaining low latency and low power consumption.

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Controlling chaotic maps using next-generation reservoir computing

Cited by 4 publications

References 33 publications

Controlling chaos using edge computing hardware

Controlling chaos using edge computing hardware

Chaos synchronization of two coupled map lattice systems using safe reinforcement learning

Photonic next-generation reservoir computer based on distributed feedback in optical fiber

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