14 citations as recorded by crossref.

1. Ensemble Kalman filter based data assimilation for tropical waves in the MJO skeleton model T. Gleiter et al. 10.1002/qj.4245
2. Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation S. Pawar & O. San 10.1029/2022MS003170
3. Convergence of forecast distributions in a 100,000‐member idealised convective‐scale ensemble K. Tempest et al. 10.1002/qj.4410
4. Combining Stochastic Parameterized Reduced‐Order Models With Machine Learning for Data Assimilation and Uncertainty Quantification With Partial Observations C. Mou et al. 10.1029/2022MS003597
5. Towards neural Earth system modelling by integrating artificial intelligence in Earth system science C. Irrgang et al. 10.1038/s42256-021-00374-3
6. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
7. Power line fault diagnosis based on convolutional neural networks L. Ning & D. Pei 10.1016/j.heliyon.2024.e29021
8. Combining data assimilation and machine learning to estimate parameters of a convective‐scale model S. Legler & T. Janjić 10.1002/qj.4235
9. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms B. Buongiorno Nardelli et al. 10.3390/rs14051159
10. Integrating Recurrent Neural Networks With Data Assimilation for Scalable Data‐Driven State Estimation S. Penny et al. 10.1029/2021MS002843
11. Weakly Constrained LETKF for Estimation of Hydrometeor Variables in Convective‐Scale Data Assimilation T. Janjić & Y. Zeng 10.1029/2021GL094962
12. Advancements and Challenges in Deep Learning-Driven Marine Data Assimilation: A Comprehensive Review Y. Ma et al. 10.61186/crpase.9.4.2876
13. Robot control system based on deep learning and RPA Y. Ren et al. 10.3233/JIFS-233056
14. Using neural networks to improve simulations in the gray zone R. Kriegmair et al. 10.5194/npg-29-171-2022