The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest.
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.
The variational cost and the gradient-based solver are both stated as neural networks using automatic differentiation for the latter.
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.
1 code implementation • 21 Jan 2020 • Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard, Jocelyn Chanussot, Lucas. Drumetz, Jean-Yves Tourneret, Alina Zare, Christian Jutten
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.
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.
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.
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.