activity
20192024
most citedCombining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model

206 citations · 594 across the 12 of their papers we have counts for

collaborators
Showing 2019Show all

2 papers · 1 filter

physics.data-an2019★ 2 cited

Representing ill-known parts of a numerical model using a machine learning approach

Julien Brajard, Anastase Charantonis, Jérôme Sirven

In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-…

eess.SY2019★ 50 cited

Learning Dynamical Systems from Partial Observations

Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot +2

We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state. We propose a natural dat…