Search Results for author:
Found 48 papers, 22 papers with code
Neural Incremental Data Assimilation
It aims to estimate the state of a potentially large system, such as the atmosphere, from sparse observations, supplemented by prior physical knowledge.
SPDE priors for uncertainty quantification of end-to-end neural data assimilation schemes
Recent advances in the deep learning community also enables to adress this problem as neural architecture embedding data assimilation variational framework.
Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields
We show that in-situ observations with richer temporal resolution represent an added value in terms of the model reconstruction performance.
Online Calibration of Deep Learning Sub-Models for Hybrid Numerical Modeling Systems
Most of these efforts in defining hybrid dynamical systems follow {offline} learning strategies in which the neural parameterization (called here sub-model) is trained to output an ideal correction.
Neural SPDE solver for uncertainty quantification in high-dimensional space-time dynamics
Recent advancements in deep learning also addressed this issue by incorporating data assimilation into neural architectures: it treats the reconstruction task as a joint learning problem involving both prior model and solver as neural networks.
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.
Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting
To this end, we fuse the spatiotemporal features from the AIS messages with probabilistic features engineered from historical AIS data referring to potential routes and destinations.
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.
A VAE Approach to Sample Multivariate Extremes
We illustrate the relevance of our approach on a synthetic data set and on a real data set of discharge measurements along the Danube river network.
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks
By carefully building a large dataset of SAR observations from the Copernicus Sentinel-1 mission, collocated with both GMF and atmospheric model wind speeds as well as rainfall estimates, we were able to train a wind speed estimator with reduced errors under rain.
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.
Deep learning for Lagrangian drift simulation at the sea surface
We address Lagrangian drift simulation in geophysical dynamics and explore deep learning approaches to overcome known limitations of state-of-the-art model-based and Markovian approaches in terms of computational complexity and error propagation.
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.
Learning-based estimation of in-situ wind speed from underwater acoustics
As sea surface winds produce sounds that propagate underwater, underwater acoustics recordings can also deliver fine-grained wind-related information.
Rain regime segmentation of Sentinel-1 observation learning from NEXRAD collocations with Convolution Neural Networks
SAR satellites deliver very high resolution observations with a global coverage.
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.
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 Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction
While the Automatic Identification System (AIS) offers a rich source of information to address this task, forecasting vessel trajectory using AIS data remains challenging, even for modern machine learning techniques, because of the inherent heterogeneous and multimodal 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.
