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Found 5 papers, 3 papers with code
Climate-Invariant Machine Learning
Data-driven algorithms, in particular neural networks, can emulate the effects of unresolved processes in coarse-resolution climate models when trained on high-resolution simulation data; however, they often make large generalization errors when evaluated in conditions they were not trained on.
Generative Modeling for Atmospheric Convection
While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of computational resources.
Towards Physically-consistent, Data-driven Models of Convection
Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations.
Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints.
Computational Physics Atmospheric and Oceanic Physics
Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models.
