Learning Variational Data Assimilation Models and Solvers

2 code implementations • 25 Jul 2020 • Ronan Fablet, Bertrand Chapron, Lucas. Drumetz, Etienne Memin, Olivier Pannekoucke, Francois Rousseau

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.

PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations

1 code implementation • 3 Feb 2020 • Olivier Pannekoucke, Ronan Fablet

While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network architectures is an open issue.

Uncertainty Quantification