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Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
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.
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Mastering the game of Go with deep neural networks and tree search
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Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956
Cited by
Machine Learning for Fluid Mechanics
Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063
Data-driven discovery of coordinates and governing equations
Champion, K., Lusch, B., Kutz, J. N., & Brunton, S. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906995116 · Cited by 909
Temporal data classification and forecasting using a memristor-based reservoir computing system
Moon, J., Ma, W., Shin, J. H., Cai, F., Du, C., Lee, S. H., & Lu, W. D. (2019) · Nature Electronics · doi:10.1038/s41928-019-0313-3 · Cited by 645
Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
(2021) · doi:10.1038/s41467-020-20692-1 · Cited by 597
Cheng, S., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., et al. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123537 · Cited by 303
Metadata
- Authors
- Pathak, J., Hunt, B., Girvan, M., Lu, Z., & Ott, E.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2018-01-12
- Volume
- 120
- Issue
- 2
- Pages
- 024102-024102
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2782714865
- Cited by
- 1,303
- References
- 6
- FWCI
- 71.29
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pathak, J., Hunt, B., Girvan, M., Lu, Z., & Ott, E. (2018). Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach. Physical Review Letters. https://doi.org/10.1103/physrevlett.120.024102
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