Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing

• URL: http://arxiv.org/abs/2203.13294v1
• Date: Thu, 24 Mar 2022 18:42:12 GMT
• Title: Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing
• Authors: Wendson A. S. Barbosa and Daniel J. Gauthier
• Abstract summary: We show that an ML architecture combined with a next-generation chaos reservoir computer displays state-of-the-art performance with a training time $103-10$4 times faster. We also take advantage of the translational symmetry of the model to further reduce the computational cost and training data, each by a factor of $sim$10.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forecasting the behavior of high-dimensional dynamical systems using machine learning (ML) requires efficient methods to learn the underlying physical model. We demonstrate spatiotemporal chaos prediction of a heuristic atmospheric weather model using an ML architecture that, when combined with a next-generation reservoir computer, displays state-of-the-art performance with a training time $10^3-10^4$ times faster and training data set $\sim 10^2$ times smaller than other ML algorithms. We also take advantage of the translational symmetry of the model to further reduce the computational cost and training data, each by a factor of $\sim$10.

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