A data-driven ABL flux parameterization using convolution operators on mean profiles, trained and tested on LES, improves on standard K-profile closures while remaining interpretable.
Zijie Guo, Pumeng Lyu, et al
3 Pith papers cite this work. Polarity classification is still indexing.
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Samudra 2 scales autoregressive neural ocean emulators to finer resolutions with architectural tweaks and dynamic loss, raising upper-ocean temperature R² from 0.56 to 0.87 at 1° and recovering mesoscale features.
Explores theoretical and data-driven closures for ocean mesoscale eddies and examines their connections using analytical and data-driven methods.
citing papers explorer
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Data-Driven Flux Parameterization for the Atmospheric Boundary Layer
A data-driven ABL flux parameterization using convolution operators on mean profiles, trained and tested on LES, improves on standard K-profile closures while remaining interpretable.
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Samudra 2: Scaling Ocean Emulators across Resolutions
Samudra 2 scales autoregressive neural ocean emulators to finer resolutions with architectural tweaks and dynamic loss, raising upper-ocean temperature R² from 0.56 to 0.87 at 1° and recovering mesoscale features.
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Towards bridging the gap between data-driven and theoretical turbulence closures in stratified flows
Explores theoretical and data-driven closures for ocean mesoscale eddies and examines their connections using analytical and data-driven methods.