Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
Jaideep Pathak, Brian R. Hunt, Michelle Girvan, Zhixin Lu, Edward Ott
Abstract
We demonstrate the effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from observations of the system's past evolution. We present a parallel scheme with an example implementation based on the reservoir computing paradigm and demonstrate the scalability of our scheme using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.
Topics & Concepts
AttractorReservoir computingChaoticScalabilityComputer scienceScheme (mathematics)Chaotic systemsStatistical physicsDimension (graph theory)Distributed computingComputational sciencePhysicsArtificial neural networkArtificial intelligenceMathematicsRecurrent neural networkPure mathematicsDatabaseMathematical analysisNeural Networks and Reservoir ComputingNeural Networks and ApplicationsModel Reduction and Neural Networks