Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers
Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse‐Chun Chen, Henry D. I. Abarbanel
Abstract
Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems. The training enforces dynamical invariants-such as the Lyapunov exponent spectrum and the fractal dimension-in the systems of interest, enabling longer and more stable forecasts when operating with limited data. The technique is demonstrated in detail using reservoir computing, a specific kind of recurrent neural network. Results are given for the Lorenz 1996 chaotic dynamical system and a spectral quasi-geostrophic model of the atmosphere, both typical test cases for numerical weather prediction.
Topics & Concepts
Lyapunov exponentChaoticDynamical systems theoryFractalLorenz systemButterfly effectComputer scienceErgodic theoryChaos theoryDimension (graph theory)Dynamical system (definition)CHAOS (operating system)Statistical physicsArtificial neural networkFractal dimensionApplied mathematicsControl theory (sociology)Artificial intelligenceMathematicsMathematical analysisPhysicsPure mathematicsControl (management)Computer securityQuantum mechanicsNeural Networks and Reservoir ComputingNeural Networks and ApplicationsModel Reduction and Neural Networks