Search Results for author:
Found 8 papers, 2 papers with code
OceanBench: The Sea Surface Height Edition
It provides plug-and-play data and pre-configured pipelines for ML researchers to benchmark their models and a transparent configurable framework for researchers to customize and extend the pipeline for their tasks.
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.
Learning Neural Optimal Interpolation Models and Solvers
The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregularly-sampled and noisy observations.
4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry
We introduce a parametrization of the 4DVarNet scheme dedicated to the space-time interpolation of satellite altimeter data.
Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies
We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations.
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.
Reversible designs for extreme memory cost reduction of CNN training
This new kind of architecture enables training large neural networks on very limited memory, opening the door for neural network training on embedded devices or non-specialized hardware.
