17 citations · 24 across the 4 of their papers we have counts for
4 papers · 1 filter
NORi: An ML-Augmented Ocean Boundary Layer Parameterization
Xin Kai Lee, Ali Ramadhan, Andre Souza +4
NORi is a machine learning (ML) parameterization of ocean boundary layer turbulence that is physics-based and augmented with neural networks. NORi stands for neural ordinary differ…
High-level, efficient ocean modeling at all scales with Oceananigans
Gregory L. Wagner, Simone Silvestri, Navid C. Constantinou +12
We describe the user interface, governing equations, and numerical methods underpinning the ocean modeling software called "Oceananigans". Oceananigans development has been led by…
Oceananigans.jl: A Julia library that achieves breakthrough resolution, memory and energy efficiency in global ocean simulations
Simone Silvestri, Gregory L. Wagner, Christopher Hill +10
Climate models must simulate hundreds of future scenarios for hundreds of years at coarse resolutions, and a handful of high-resolution decadal simulations to resolve localized ext…
Capturing missing physics in climate model parameterizations using neural differential equations
Ali Ramadhan, John Marshall, Andre Souza +8
We explore how neural differential equations (NDEs) may be trained on highly resolved fluid-dynamical models of unresolved scales providing an ideal framework for data-driven param…