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Found 27 papers, 16 papers with code
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.
Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields
For numerous earth observation applications, one may benefit from various satellite sensors to address the reconstruction of some process or information of interest.
Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning
The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables.
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.
TrAISformer-A generative transformer for AIS trajectory prediction
Modelling trajectory in general, and vessel trajectory in particular, is a difficult task because of the multimodal and complex nature of motion data.
Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme
They are notable for the ability to include a deterministic component of the system and a stochastic one to represent random unknown factors.
Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification
Inevitably, a numerical simulation of a differential equation will then always be distinct from a true analytical solution.
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).
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations
The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest.
Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean
The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention.
Learning Variational Data Assimilation Models and Solvers
Intriguingly, we also show that the variational models issued from the true Lorenz-63 and Lorenz-96 ODE representations may not lead to the best reconstruction performance.
Joint learning of variational representations and solvers for inverse problems with partially-observed data
The variational cost and the gradient-based solver are both stated as neural networks using automatic differentiation for the latter.
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.
PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations
While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network architectures is an open issue.
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness.
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.
End-to-end learning of energy-based representations for irregularly-sampled signals and images
In this paper, we address the end-to-end learning of representations of signals, images and image sequences from irregularly-sampled data, i. e. when the training data involved missing data.
Learning Latent Dynamics for Partially-Observed Chaotic Systems
This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i. e., dynamical systems for which some components are never observed, with an emphasis on forecasting applications, including long-term asymptotic patterns.
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations
To solve for the joint inference of the hidden dynamics and of model parameters, we combine neural-network representations and state-of-the-art assimilation schemes.
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection.
Ranked #2 on
Acoustic Novelty Detection
on A3Lab PASCAL CHiME
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
In a world of global trading, maritime safety, security and efficiency are crucial issues.
Sea surface temperature prediction and reconstruction using patch-level neural network representations
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges.
Residual Networks as Geodesic Flows of Diffeomorphisms
This paper addresses the understanding and characterization of residual networks (ResNet), which are among the state-of-the-art deep learning architectures for a variety of supervised learning problems.
Bilinear residual Neural Network for the identification and forecasting of dynamical systems
Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches.
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).
Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data
Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement.
