Abstract
Forecasting of spatiotemporal chaotic dynamical systems is an important problem in several scientific fields. Crucial scientific applications such as weather forecasting and climate modeling depend on the ability to effectively model spatiotemporal chaotic geophysical systems such as the atmosphere and oceans. Recent advances in the field of machine learning have the potential to be an important tool for modeling such systems. In this chapter, we review several key ideas and discuss some reservoir-computing-based architectures for purely data-driven as well as hybrid data-assisted forecasting of chaotic systems with an emphasis on scalability to large, high-dimensional systems.
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Acknowledgements
We thank our colleagues, particularly Brian Hunt, Michelle Girvan, and Istvan Szunyogh, for their contributions. We also acknowledge support from DARPA contract HR00111890044.
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Pathak, J., Ott, E. (2021). Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems. In: Nakajima, K., Fischer, I. (eds) Reservoir Computing. Natural Computing Series. Springer, Singapore. https://doi.org/10.1007/978-981-13-1687-6_6
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