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Area of Science:

  • Artificial Intelligence
  • Computational Physics
  • Dynamical Systems

Background:

  • Reservoir computing (RC) is a machine learning technique for time-series forecasting.
  • Forecasting chaotic systems, like the Lorenz '63 attractor, is challenging.
  • Existing reservoir computer designs often assume high connectivity and recurrence.

Purpose of the Study:

  • To explore the hyperparameter space of reservoir computers for forecasting the Lorenz '63 attractor.
  • To introduce and utilize a novel performance measure emphasizing global climate reproduction over short-term prediction.
  • To investigate the impact of network connectivity and structure on reservoir performance.

Main Methods:

  • Bayesian optimization was employed to efficiently search the hyperparameter space.
  • A new reservoir performance metric was developed, focusing on climate accuracy.
  • Simulations were conducted using the chaotic Lorenz '63 system.
  • Optimized reservoir structures were analyzed for connectivity and recurrence properties.

Main Results:

  • Optimizing with the new metric rapidly identified underperforming reservoirs.
  • Optimized reservoir parameters frequently resulted in networks with very low connectivity.
  • Further exploration revealed well-performing reservoirs with zero spectral radius and no recurrence.
  • These findings challenge conventional wisdom regarding reservoir design.

Conclusions:

  • Simple reservoir computer designs with minimal connectivity can be highly effective for chaotic system forecasting.
  • The developed performance measure aids in discovering efficient reservoir configurations.
  • These findings offer valuable insights for hardware implementations of reservoir computing.