System identification based on sparse approximation of Koopman operator
Author:
Funder
Natural Science Basic Research Program of Shaanxi Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1140/epjs/s11734-024-01264-6.pdf
Reference43 articles.
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2. J. Nathan Kutz, J.L. Proctor, S.L. Brunton, Applied Koopman theory for partial differential equations and data-driven modeling of spatio-temporal systems. Complexity 2018, 1–16 (2018). https://doi.org/10.1155/2018/6010634
3. E. Melas, C. Poulios, E. Camouzis, J. Leventides, N. Poulios, Study of the hypergeometric equation via data driven Koopman-EDMD theory. Axioms 12(2), 134 (2023). https://doi.org/10.3390/axioms12020134
4. Y. Wang, Y. Yuan, H. Fang, H. Ding, Data-driven discovery of linear dynamical systems from noisy data. Sci. China Technol. Sci. 67, 121–129 (2024). https://doi.org/10.1007/s11431-023-2520-6
5. W. Sun, J. Feng, J. Su, Y. Liang, Data driven adaptive Gaussian mixture model for solving Fokker-Planck equation. Chaos 32(3), 033131 (2022). https://doi.org/10.1063/5.0083822
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