11 citations as recorded by crossref.

1. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
2. Reconstructing Attractors of a Conceptual Airfoil System via Next Generation Reservoir Computing Q. Liu et al. 10.2514/1.J064609
3. Application of Multiparameter Kalman Filter in Ultrasonic Water Meter F. Zuo et al. 10.1155/2022/3580376
4. Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations G. Gottwald & S. Reich 10.1063/5.0066080
5. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
6. Mutil-medical image encryption by a new spatiotemporal chaos model and DNA new computing for information security H. Liu et al. 10.1016/j.eswa.2023.121090
7. Lorenz’s View on the Predictability Limit of the Atmosphere B. Shen et al. 10.3390/encyclopedia3030063
8. An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation Y. Sawada 10.1029/2021MS002882
9. An Innovative Algorithm Based on Chaotic Maps Amalgamated with Bit-Level Permutations for Robust S-Box Construction and Its Application in Medical Image Privacy M. Hazzazi et al. 10.3390/sym16081070
10. Data assimilation with hybrid modeling D. Shao et al. 10.1016/j.chaos.2022.113069
11. Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components A. Wikner et al. 10.1063/5.0048050