Search Results for author:
Found 8 papers, 7 papers with code
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.
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.
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image.
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.
