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Found 12 papers, 6 papers with code
Gradient-free online learning of subgrid-scale dynamics with neural emulators
It is demonstrated that training the neural emulator and parametrization components separately with different loss quantities is necessary in order to minimize the propagation of approximation biases.
Training neural mapping schemes for satellite altimetry with simulation data
Here, we leverage both simulations of ocean dynamics and satellite altimeters to train simulation-based neural mapping schemes for the sea surface height and demonstrate their performance for real altimetry datasets.
Scale-aware neural calibration for wide swath altimetry observations
Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics.
Inversion of sea surface currents from satellite-derived SST-SSH synergies with 4DVarNets
This is however limited to the surface-constrained geostrophic component of sea surface velocities.
Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
Optimal Interpolation (OI) is a widely used, highly trusted algorithm for interpolation and reconstruction problems in geosciences.
A DNN Framework for Learning Lagrangian Drift With Uncertainty
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data.
A posteriori learning for quasi-geostrophic turbulence parametrization
State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models.
A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps
Modeling the subgrid-scale dynamics of reduced models is a long standing open problem that finds application in ocean, atmosphere and climate predictions where direct numerical simulation (DNS) is impossible.
Joint calibration and mapping of satellite altimetry data using trainable variational models
The proposed framework significantly outperforms the operational state-of-the-art mapping pipeline and truly benefits from wide-swath data to resolve finer scales on the global map as well as in the SWOT sensor geometry.
Physical invariance in neural networks for subgrid-scale scalar flux modeling
In this paper we present a new strategy to model the subgrid-scale scalar flux in a three-dimensional turbulent incompressible flow using physics-informed neural networks (NNs).
Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSH), thus allowing for a better characterization of the mesoscale and submesoscale eddy field.
Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models
We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems.
