11 citations as recorded by crossref.

1. Machine learning for weather and climate are worlds apart D. Watson-Parris 10.1098/rsta.2020.0098
2. From rain to data: A review of the creation of monthly and daily station‐based gridded precipitation datasets R. Serrano‐Notivoli & E. Tejedor 10.1002/wat2.1555
3. Emulating radiative transfer with artificial neural networks S. Sethuram et al. 10.1093/mnras/stad2524
4. Partial differential equations discovery with EPDE framework: Application for real and synthetic data M. Maslyaev et al. 10.1016/j.jocs.2021.101345
5. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. 10.2166/wst.2020.369
6. Statistical mechanics in climate emulation: Challenges and perspectives I. Sudakow et al. 10.1017/eds.2022.15
7. Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output B. Kravitz et al. 10.1371/journal.pclm.0000159
8. Performance assessment for climate intervention (PACI): preliminary application to a stratospheric aerosol injection scenario L. Wheeler et al. 10.3389/fenvs.2023.1205515
9. A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies A. Thelen et al. 10.1007/s00158-022-03425-4
10. Machine learning for faster estimates of groundwater response to artificial aquifer recharge V. Fernandes et al. 10.1016/j.jhydrol.2024.131418
11. Surrogate modelling of a detailed farm‐level model using deep learning L. Shang et al. 10.1111/1477-9552.12543