State Estimation of Partially Unknown Dynamical Systems with a Deep Kalman Filter
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63775-9_22
Reference15 articles.
1. Brunton, S.L., Budisic, M., Kaiser, E., Kutz, J.N.: Modern Koopman theory for dynamical systems. SIAM Rev. 64(2), 229–340 (2022). https://doi.org/10.1137/21M1401243
2. Cheng, S., et al.: Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review. IEEE/CAA J. Automatica Sin. 10 (2023). https://doi.org/10.1109/JAS.2023.123537
3. Chinellato, E., Marcuzzi, F., Pierobon, S.: Physics-aware soft-sensors for embedded digital twins. Springer LNNS, in press (2024)
4. Dessole, M., Marcuzzi, F.: Accurate detection of hidden material changes as fictitious heat sources. Numer. Heat Transfer, Part B: Fundam. (2023). https://doi.org/10.1080/10407790.2023.2220905
5. Fasel, U., Kutz, J.N., Brunton, B.W., Brunton, S.L.: Ensemble-SINDy: robust sparse model discovery in the low-data, high-noise limit, with active learning and control. Proc. R. Soc. A. (2022). https://doi.org/10.1098/rspa.2021.0904
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