Machine learning algorithms for predicting the amplitude of chaotic laser pulses

Amil, Pablo; Soriano, Miguel C.; Masoller, Cristina

doi:10.1063/1.5120755

Cited by 66 publications

(28 citation statements)

References 34 publications

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“…Many different realizations have been presented in the last years, ranging from a bucket of water [4] over field programmable gate arrays (FPGAs) [5] to dissociated neural cell cultures [6], being used for satellite communications [7], real-time audio processing [8,9], bit-error correction for optical data transmission [10], amplitude of chaotic laser pulse prediction [11] and cross-predicting the dynamics of an injected laser [12]. Especially opto-electronic [13,14] and optical setups [15][16][17][18][19] were frequently studied because their high speed and low energy consumption make them preferable for hardware realizations.…”

Section: Introductionmentioning

confidence: 99%

Limitations of the Recall Capabilities in Delay-Based Reservoir Computing Systems

2020

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We analyse the memory capacity of a delay-based reservoir computer with a Hopf normal form as nonlinearity and numerically compute the linear as well as the higher order recall capabilities. A possible physical realization could be a laser with external cavity, for which the information is fed via electrical injection. A task-independent quantification of the computational capability of the reservoir system is done via a complete orthonormal set of basis functions. Our results suggest that even for constant readout dimension the total memory capacity is dependent on the ratio between the information input period, also called the clock cycle, and the time delay in the system. Optimal performance is found for a time delay about 1.6 times the clock cycle.

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Section: Introductionmentioning

confidence: 99%

Limitations of the Recall Capabilities in Delay-Based Reservoir Computing Systems

2020

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“…The application of machine learning techniques has been extended to even more complex systems such as those observed in transient laser behaviour and extreme events [77].…”

Section: Chaotic Systems and Instabilitiesmentioning

confidence: 99%

“…Specifically, using the knowledge of previous pulses in a chaotic time series from an optically injected semiconductor laser operating, machine learning methods (nearest neighbors, support vector machine, feed-forward neural networks, reservoir computing) were analyzed for their ability to predict the intensity of upcoming pulses emitted from the laser [77,78]. Although this work was numerical, it clearly shows the potential of such prediction in experiment.…”

Section: Chaotic Systems and Instabilitiesmentioning

confidence: 99%

Machine learning and applications in ultrafast photonics

et al. 2020

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Recent years have seen the rapid growth and development of the field of smart photonics, where machine learning algorithms are being matched to optical systems to add new functionalities and to enhance performance. An area where machine learning shows particular potential to accelerate technology is the field of ultrafast photonics -the generation and characterization of light pulses, the study of light-matter interactions on short timescales, and high-speed optical measurements. Our aim here is to highlight a number of specific areas where the promise of machine learning in ultrafast photonics has already been realized, including the design and operation of pulsed lasers, and the characterization and control of ultrafast propagation dynamics. We also consider challenges and future areas of research.

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“…Since the spatiotemporal chaos generated in the resonator is a highly dimensional one, (∆T stc ∆T p and τ stc < t R ), forecasting the fully developed turbulence of the fiber ring cavity is a great challenge. Recent works using neural networks have opened new perspectives in this field [9,[42][43][44][45]. In particular, in [9,42], they have used an echo state network to reproduce the dynamics of the Kuramoto-Shivashinsky equation over several Lyapunov times.…”

Section: Precursors-driven Machine Learningmentioning

confidence: 99%

Precursor-driven machine learning prediction of chaotic extreme pulses in Kerr resonators

Coulibaly,

Bessin,

Clerc

et al. 2022

Preprint

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Machine learning algorithms have opened a breach in the fortress of the prediction of high-dimensional chaotic systems. Their ability to find hidden correlations in data can be exploited to perform model-free forecasting of spatiotemporal chaos and extreme events. However, the extensive feature of these evolutions constitutes a critical limitation for full-size forecasting processes. Hence, the main challenge for forecasting relevant events is to establish the set of pertinent information. Here, we identify precursors from the transfer entropy of the system and a deep Long Short-Term Memory network to forecast the complex dynamics of a system evolving in a high-dimensional spatiotemporal chaotic regime. Performances of this triggerable model-free prediction protocol based on the information flowing map are tested from experimental data originating from a passive resonator operating in such a complex nonlinear regime. We have been able to predict the occurrence of extreme events up to 9 round trips after the detection of precursor, i.e., 3 times the horizon provided by Lyapunov exponents, with 92 % of true positive predictions leading to 60 % of accuracy.

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Machine learning algorithms for predicting the amplitude of chaotic laser pulses

Abstract: X-ray spectroscopy evidence for plasma shell formation in experiments modeling accretion columns in young stars Matter and Radiation at Extremes 4, 064402 (2019);

Cited by 66 publications

References 34 publications

Limitations of the Recall Capabilities in Delay-Based Reservoir Computing Systems

Limitations of the Recall Capabilities in Delay-Based Reservoir Computing Systems

Machine learning and applications in ultrafast photonics

Precursor-driven machine learning prediction of chaotic extreme pulses in Kerr resonators

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