Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers
Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers
Explore innovative machine learning forecasting with ergodic theory for chaotic systems, ensuring stable, long-term predictions by J. A. Platt and team.
Sofar Ocean
Oct 3, 2023
Abstract
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.