39 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.
2. Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2) E. Sinha et al.
3. Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling A. Chen et al.
4. Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al.
5. A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States R. Qin et al.
6. GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI M. Fan et al.
7. Integrating plant physiology into simulation of fire behavior and effects L. Dickman et al.
8. Machine learning emulators of dynamical systems for understanding ecosystem behaviour O. Pomarol Moya et al.
9. Assessing and enhancing Noah-MP land surface modeling over tropical forests using machine learning techniques Y. Cheng et al.
10. Emulator-based calibration of a dynamic grassland model using recurrent neural networks and Hamiltonian Monte Carlo V. Aakula et al.
11. 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.
12. Angle information enhanced kriging-assisted adaptive evolutionary algorithm for computationally highly expensive high-dimensional problems H. Lu et al.
13. Surrogate construction via weight parameterization of residual neural networks O. Diaz-Ibarra et al.
14. Assessing human health risk of groundwater DNAPL contamination by quantifying the model structure uncertainty Y. Pan et al.
15. Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds S. Li et al.
16. Toward real-time high-resolution fluvial flood forecasting: A robust surrogate approach based on overland flow models G. Nguyen et al.
17. A scalable framework for quantifying field-level agricultural carbon outcomes K. Guan et al.
18. Hector V3.2.0: functionality and performance of a reduced-complexity climate model K. Dorheim et al.
19. Removal of Powerline Noise in Geophysical Datasets With a Scientific Machine-Learning Based Approach J. Larsen et al.
20. 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.
21. Exploring the potential of history matching for land surface model calibration N. Raoult et al.
22. 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.
23. Technical note: Deep learning for creating surrogate models of precipitation in Earth system models T. Weber et al.
24. RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al.
25. Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al.
26. Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models M. Fischer et al.
27. A tree location optimization via metamodel-based simulation framework for mitigating extreme heat in communities Y. Bai et al.
28. GALDS: A graph-autoencoder-based latent dynamics surrogate model to predict neurite material transport T. Hsieh & Y. Zhang
29. Predicting Concentrations of PM2.5, PM10, CO, VOC, and NOx on the Urban Scale Using Machine Learning-Based Surrogate Models P. Lewicki et al.
30. An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska F. Yuan et al.
31. 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
32. Toward Stable, General Machine‐Learned Models of the Atmospheric Chemical System M. Kelp et al.
33. 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.
34. Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling R. Sun et al.
35. 100 years of data is not enough to establish reliable drought thresholds R. Link et al.
36. 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
37. Bridging the gap between mechanistic biological models and machine learning surrogates I. Gherman et al.
38. A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al.
39. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al.