Search Results for author:
Found 13 papers, 9 papers with code
Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning
To understand changes in the Antarctic Circumpolar Current, we extend the method Tracking global Heating with Ocean Regimes (THOR) to a mesoscale eddy permitting climate model and identify regions of the ocean characterized by similar physics, called dynamical regimes, using readily accessible fields from climate models.
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.
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 Matching (HM) to tune a climate system with multi-scale dynamics.
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics
The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network.
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.
Bridging observation, theory and numerical simulation of the ocean using Machine Learning
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study.
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night.
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.
EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
This work presents EddyNet, a deep learning based architecture for automated eddy detection and classification from Sea Surface Height (SSH) maps provided by the Copernicus Marine and Environment Monitoring Service (CMEMS).
Reinforcement Learning Based Algorithm for the Maximization of EV Charging Station Revenue
This paper presents an online reinforcement learning based application which increases the revenue of one particular electric vehicles (EV) station, connected to a renewable source of energy.
