Acknowledgement
이 논문은 부경대학교 자율창의학술연구비(2021년)에 의하여 연구되었음.
References
- Abarbanel, H.D., P.J. Rozdeba and S. Shirman, 2018. Machine Learning: Deepest learning as statistical data assimilation problems. Neural Computation, 30(8): 2025-2055. DOI: https://doi.org/10.1162/neco_a_01094.
- Brajard, J., A. Carrassi, M. Bocquet and L. Bertino, 2019. Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model. Geoscientific Model Development Discussions, 44: 1-21. DOI: https://doi.org/10.1016/j.jocs.2020.101171.
- Chattopadhyay, A., P. Hassanzadeh and D. Subramanian, 2020a. Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network, Nonlin. Processes Geophys., 27(3): 373-389, DOI: https://doi.org/10.5194/npg-27-373-2020.
- Chattopadhyay, A., A. Subel and P. Hassanzadeh, 2020b. Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning, arXiv [preprint], arXiv:2002.11167.
- Dubois, P., T. Gomez, L. Planckaert and L. Perret, 2020. Data-driven predictions of the Lorenz system. Physica D: Nonlinear Phenomena, 408: 132495. DOI: 10.1016/j.physd.2020.132495.
- Erichson, N.B., L. Mathelin, Z. Yao, S.L. Brunton, M.W. Mahoney and J.N. Kutz, 2019. Shallow learning for fluid flow reconstruction with limited sensors and limited data, arXiv preprint arXiv:1902.07358.
- Ham, Y.-G., J.-H. Kim and J.-J. Luo, 2019. Deep learning for multi-year ENSO forecasts. Nature, 573: 568-572. DOI: 10.1038/s41586-019-1559-7.
- Kingma, D.P. and J. Ba, 2014. Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980.
- Kutz, J.N., 2013. Data-driven modeling & scientific computation: methods for complex systems & big data, Oxford University Press, 656 pp.
- Lorenz, E.N., 1963. Deterministic nonperiodic flow. Journal of the atmospheric sciences, 20(2): 130-141. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
- Scher, S. and G. Messori, 2019. Generalization properties of feed-forward neural networks trained on Lorenz systems. Nonlin. Processes Geophys., 26(4): 381-399. DOI: https://doi.org/10.5194/npg-26-381-2019.
- Wang, J. and Y. Li, 2018. Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy. Applied Energy, 230: 429-443. https://doi.org/10.1016/j.apenergy.2018.08.114
- Yu, R., S. Zheng and Y. Liu, 2017. Learning chaotic dynamics using tensor recurrent neural networks. in: Proceedings of the ICML, Vol. 17.
- Zhang, J.-S. and X.-C. Xiao, 2000. Predicting chaotic time series using recurrent neural network. Chinese Physics Letters, 17(2): 88.