Articles | Volume 18, issue 18
https://doi.org/10.5194/gmd-18-6275-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Model description paper
| 
24 Sep 2025
Model description paper |
| 24 Sep 2025
| 24 Sep 2025
A dynamic informed deep-learning method for future estimation of laboratory stick–slip
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Cited articles
Arbabi, H. and Mezić, I.: Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator, SIAM J. Appl. Dyn. Syst., 16, 2096–2126, https://doi.org/10.1137/17M1125236, 2017.
Avila, A. M. and Mezić, I.: Data-driven analysis and forecasting of highway traffic dynamics, Nat. Commun., 11, 2090, https://doi.org/10.1038/s41467-020-15582-5, 2020.
Azencot, O., Erichson, N. B., Lin, V., and Mahoney, M. W.: Forecasting sequential data using consistent Koopman autoencoders, https://doi.org/10.48550/arXiv.2003.02236, 2020.
Bai, S., Kolter, J. Z., and Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, https://doi.org/10.48550/arXiv.1803.01271, 2018.
Bakarji, J., Champion, K., Kutz, J. N., and Brunton, S. L.: Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders, Proc. Roy. Soc. A, 479, 20230422, https://doi.org/10.1098/rspa.2023.0422, 2023.
