Towards Discovery of the Differential Equations
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Published:2023-12
Issue:S2
Volume:108
Page:S257-S264
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ISSN:1064-5624
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Container-title:Doklady Mathematics
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language:en
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Short-container-title:Dokl. Math.
Author:
Hvatov A. A.,Titov R. V.
Publisher
Pleiades Publishing Ltd
Reference17 articles.
1. S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Discovering governing equations from data by sparse identification of nonlinear dynamical systems,” Proc. Natl. Acad. Sci. U. S. A. 113, 3932–3937 (2016). https://doi.org/10.1073/pnas.1517384113 2. S. H. Rudy, S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Data-driven discovery of partial differential equations,” Sci. Adv. 3, e1602614 (2017). https://doi.org/10.1126/sciadv.1602614 3. D. A. Messenger and D. M. Bortz, “Weak SINDy for partial differential equations,” J. Comput. Phys. 443, 110525 (2021). https://doi.org/10.1016/j.jcp.2021.110525 4. U. Fasel, J. N. Kutz, B. W. Brunton, and S. L. Brunton, “Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control,” Proc. R. Soc. A: Math., Phys. Eng. Sci. 478, 20210904 (2022). https://doi.org/10.1098/rspa.2021.0904 5. Z. Long, Yi. Lu, and B. Dong, “PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network,” J. Comput. Phys. 399, 108925 (2019). https://doi.org/10.1016/j.jcp.2019.108925
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