31 citations as recorded by crossref.

1. Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux? M. Moghaddam et al. 10.3390/w13202837
2. Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2) E. Sinha et al. 10.1029/2022MS003171
3. Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling A. Chen et al. 10.1029/2022MS003135
4. Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al. 10.1016/j.xinn.2024.100691
5. A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States R. Qin et al. 10.1016/j.agrformet.2025.110407
6. Integrating plant physiology into simulation of fire behavior and effects L. Dickman et al. 10.1111/nph.18770
7. Machine learning emulators of dynamical systems for understanding ecosystem behaviour O. Pomarol Moya et al. 10.1016/j.ecolmodel.2024.110956
8. A Computationally Efficient, Time-Dependent Model of the Solar Wind for Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic Simulations M. Owens et al. 10.1007/s11207-020-01605-3
9. Surrogate construction via weight parameterization of residual neural networks O. Diaz-Ibarra et al. 10.1016/j.cma.2024.117468
10. Assessing human health risk of groundwater DNAPL contamination by quantifying the model structure uncertainty Y. Pan et al. 10.1016/j.jhydrol.2020.124690
11. Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds S. Li et al. 10.1016/j.envsoft.2021.105083
12. A scalable framework for quantifying field-level agricultural carbon outcomes K. Guan et al. 10.1016/j.earscirev.2023.104462
13. Hector V3.2.0: functionality and performance of a reduced-complexity climate model K. Dorheim et al. 10.5194/gmd-17-4855-2024
14. Removal of Powerline Noise in Geophysical Datasets With a Scientific Machine-Learning Based Approach J. Larsen et al. 10.1109/TGRS.2022.3223737
15. Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution M. Khan et al. 10.1016/j.jenvman.2024.121296
16. Exploring the potential of history matching for land surface model calibration N. Raoult et al. 10.5194/gmd-17-5779-2024
17. Extending a land-surface model with Sphagnum moss to simulate responses of a northern temperate bog to whole ecosystem warming and elevated CO2 X. Shi et al. 10.5194/bg-18-467-2021
18. Technical note: Deep learning for creating surrogate models of precipitation in Earth system models T. Weber et al. 10.5194/acp-20-2303-2020
19. Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al. 10.5194/gmd-15-1913-2022
20. Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models M. Fischer et al. 10.5194/wcd-5-511-2024
21. An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska F. Yuan et al. 10.1016/j.jocs.2023.102145
22. Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich 10.3390/cli12110189
23. Toward Stable, General Machine‐Learned Models of the Atmospheric Chemical System M. Kelp et al. 10.1029/2020JD032759
24. Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling S. Li et al. 10.1021/acsestengg.1c00179
25. Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling R. Sun et al. 10.1029/2020WR028740
26. 100 years of data is not enough to establish reliable drought thresholds R. Link et al. 10.1016/j.hydroa.2020.100052
27. Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost E. Sahin 10.1007/s00477-022-02330-y
28. Bridging the gap between mechanistic biological models and machine learning surrogates I. Gherman et al. 10.1371/journal.pcbi.1010988
29. A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. 10.1186/s13321-024-00825-0
30. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. 10.1016/j.jhydrol.2023.129944
31. Surrogate Modeling Methodology for Nonlinear Atmospheric Dynamics: From Conceptual Model to Neural Networks S. Soldatenko & Y. Angudovich 10.3103/S1068373925020013