Predicting shallow water dynamics using echo-state networks with transfer learning-Reference-Cited by-同舟云学术

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Predicting shallow water dynamics using echo-state networks with transfer learning

Published:2022-11-04 Issue:1 Volume:13 Page:
ISSN:1869-2672
Container-title:GEM - International Journal on Geomathematics
language:en
Short-container-title:Int J Geomath

Author:

Chen Xiaoqian,Nadiga Balasubramanya T.,Timofeyev Ilya^ORCID

Funder

Office of Naval Research

National Science Foundation

U.S. Department of Energy

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,Modeling and Simulation

Link

https://link.springer.com/content/pdf/10.1007/s13137-022-00210-9.pdf

Reference34 articles.

1. Arcomano, T., Szunyogh, I., Pathak, J., Wikner, A., Hunt, B.R., Ott, E.: A machine learning-based global atmospheric forecast model. Geophys. Res. Lett. 47(9), 2020–087776 (2020)

2. Audusse, E., Chalons, C., Ung, P.: A simple well-balanced and positive numerical scheme for the shallow-water system. Commun. Math. Sci. 13(5), 1317–1332 (2015)

3. Berg, J., Nyström, K.: Data-driven discovery of PDEs in complex datasets. J. Comput. Phys. 384, 239–252 (2019)

4. Bollt, E.: On explaining the surprising success of reservoir computing forecaster of chaos? the universal machine learning dynamical system with contrast to VAR and DMD. Chaos Interdiscip. J. Nonlinear Sci. 31(1), 013108 (2021). https://doi.org/10.1063/5.0024890

5. Buehner, M., Young, P.: A tighter bound for the echo state property. IEEE Trans. Neural Netw. 17(3), 820–824 (2006)

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