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A systematic exploration of reservoir computing for forecasting complex spatiotemporal dynamics.

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  • 1Department of Physics, University of California San Diego, United States of America.

Neural Networks : the Official Journal of the International Neural Network Society
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Summary

Reservoir computing (RC) enhances chaotic system prediction by optimizing architecture. Including input bias significantly improves forecast skill, crucial for numerical weather prediction models.

Keywords:
Chaotic time series forecastingEcho-state networksMachine learningNonlinear dynamical systemsRecurrent neural networkReservoir computing

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Area of Science:

  • Artificial Intelligence
  • Computational Physics
  • Dynamical Systems Theory

Background:

  • Reservoir computing (RC) is a recurrent neural network architecture adept at predicting complex spatiotemporally chaotic dynamical systems.
  • RCs can reproduce intrinsic dynamical quantities, making them valuable for numerical forecasting methods like the ensemble Kalman filter used in weather prediction.

Purpose of the Study:

  • To identify optimal architecture and design choices for a high-performance reservoir computer.
  • To investigate the impact of various design parameters on forecast skill for chaotic systems.

Main Methods:

  • Systematic exploration of reservoir computer design choices, including parameter optimization, input bias, reservoir dimension, spinup time, training data volume, normalization, noise, and time step.
  • Application of these investigations to the 40-dimensional Lorenz 1996 chaotic system.

Main Results:

  • Large-scale parameter optimization is critical for achieving 'best in class' reservoir computer performance.
  • The inclusion of input bias in the reservoir computer design significantly enhances forecast skill.
  • Nonlinear readout operators did not impact forecast time or stability in the tested configurations.

Conclusions:

  • Optimal design choices for reservoir computers involve careful consideration of parameter optimization and input bias.
  • These findings contribute to the development of advanced parallel reservoir computing schemes for complex dynamical systems.
  • The study provides a framework for designing effective reservoir computers for applications like numerical weather prediction.