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Found 2 papers, 1 papers with code
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow
For effective TL, we need to know 1) what are the best layers to re-train?
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning
To make weather/climate modeling computationally affordable, small-scale processes are usually represented in terms of the large-scale, explicitly-resolved processes using physics-based or semi-empirical parameterization schemes.
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