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11/18/2022
Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
Optimal Interpolation (OI) is a widely used, highly trusted algorithm fo...
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08/11/2022
Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model
The objective of this study is to evaluate the potential for History Mat...
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04/30/2022
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics
The trustworthiness of neural networks is often challenged because they ...
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04/08/2022
A posteriori learning for quasi-geostrophic turbulence parametrization
The use of machine learning to build subgrid parametrizations for climat...
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11/12/2021
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...
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04/26/2021
Bridging observation, theory and numerical simulation of the ocean using Machine Learning
Progress within physical oceanography has been concurrent with the incre...
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11/13/2020
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
The recent explosion in applications of machine learning to satellite im...
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10/09/2020
Physical invariance in neural networks for subgrid-scale scalar flux modeling
In this paper we present a new strategy to model the subgrid-scale scala...
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05/03/2020
Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry m...
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11/20/2019
Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models
We introduce a new strategy designed to help physicists discover hidden ...
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09/05/2018
Classification Algorithms for Semi-Blind Uplink/Downlink Decoupling in sub-6 GHz/mmWave 5G Networks
Reliability and latency challenges in future mixed sub-6 GHz/millimeter ...
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11/10/2017