Peer-Reviewed Research

Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers

Sofar Ocean

This paper was written by J. A. Platt, S. G. Penny, T. A. Smith, T. C. Chen, and H. D. I. Abarbanela.


Drawing on ergodic theory, a novel training method is introduced for machine learning-based forecasting methods for chaotic dynamical systems. The training enforces dynamical invariants—such as the Lyapunov exponent spectrum and fractal dimension—in the systems of interest, enabling longer and more stable forecasts when operating with limited data.

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