94 citations · 113 across the 4 of their papers we have counts for
4 papers
Boundary Conditions for the Parametric Kalman Filter forecast submited
M. Sabathier, O. Pannekoucke, V. Maget +1
This paper is a contribution to the exploration of the parametric Kalman filter (PKF), which is an approximation of the Kalman filter, where the error covariances are approximated…
SymPKF: a symbolic and computational toolbox for the design of parametric Kalman filter dynamics
Olivier Pannekoucke, Philippe Arbogast
Recent researches in data assimilation lead to the introduction of the parametric Kalman filter (PKF): an implementation of the Kalman filter, where the covariance matrices are app…
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…
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…