94 citations · 113 across the 4 of their papers we have counts for
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physics.comp-ph2020★ 94 cited
Learning Variational Data Assimilation Models and Solvers
Ronan Fablet, Bertrand Chapron, Lucas. Drumetz +3
This paper addresses variational data assimilation from a learning point of view. Data assimilation aims to reconstruct the time evolution of some state given a series of observati…
physics.comp-ph2020★ 18 cited
PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations
Olivier Pannekoucke, Ronan Fablet
Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network…