12 citations as recorded by crossref.

1. Minimal deterministic echo state networks outperform random reservoirs in learning chaotic dynamics F. Martinuzzi 10.1063/5.0288751
2. A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics‐Based Component T. Arcomano et al. 10.1029/2022GL102649
3. New analytic formulas for memory and prediction functions in reservoir computers with time delays P. Mullarkey & S. Marzen 10.1063/5.0272492
4. Short-term prediction of geomagnetic secular variation with an echo state network S. Nakano et al. 10.1186/s40623-024-02064-x
5. Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. 10.5194/npg-31-535-2024
6. Spatial prediction of the turbulent unsteady von Kármán vortex street using echo state networks M. Sharifi Ghazijahani et al. 10.1063/5.0172722
7. How neural networks work: Unraveling the mystery of randomized neural networks for functions and chaotic dynamical systems E. Bollt 10.1063/5.0206840
8. Circuit design in biology and machine learning. I. Random networks and dimensional reduction S. A. Frank & M. Kirkpatrick 10.1093/evolut/qpaf065
9. An error correction system for sea surface temperature prediction R. Araújo et al. 10.1007/s00521-023-08311-8
10. Emerging opportunities and challenges for the future of reservoir computing M. Yan et al. 10.1038/s41467-024-45187-1
11. Domain wall velocity prediction in magnetic nano stripe under spin-polarized current using machine learning techniques M. Sen & S. Barman 10.1007/s40042-025-01312-x
12. Lorenz’s View on the Predictability Limit of the Atmosphere B. Shen et al. 10.3390/encyclopedia3030063