396 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR154 papers
- Paris-Est SupFR126 papers
- École nationale des ponts et chausséesFR92 papers
- CERMICSFR71 papers
- Institut national de recherche en sciences et technologies du numériqueFR64 papers
- Laboratoire d'Informatique Gaspard-MongeFR46 papers
- Laboratoire d’Analyse et de Mathématiques AppliquéesFR44 papers
- Université Paris-Est CréteilFR40 papers
- Université Paris-Est Marne-la-ValléeFR39 papers
- Laboratoire NavierFR33 papers
- Laboratoire Modélisation et Simulation Multi-EchelleFR29 papers
- Université Paris DiderotFR15 papers
4 papers · 1 filter
Uniform-in-time convergence of numerical methods for non-linear degenerate parabolic equations
Jerome Droniou, Robert Eymard
Gradient schemes is a framework that enables the unified convergence analysis of many numerical methods for elliptic and parabolic partial differential equations: conforming and no…
On temporal scale separation in coupled data assimilation with the ensemble Kalman filter
Maxime Tondeur, Alberto Carrassi, Stephane Vannitsem +1
Coupled data assimilation (CDA) distinctively appears as a main concern in numerical weather and climate prediction with major efforts put forward worldwide. The core issue is the…
Multilayer models for shallow two-phase debris flows with dilatancy effects
José Garres-Díaz, François Bouchut, Enrique D. Fernández-Nieto +2
We present here a multilayer model for shallow grain-fluid mixtures with dilatancy effects. It can be seen as a generalization of the depth-averaged model presented in Bouchut et a…
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
Julien Brajard, Alberto Carassi, Marc Bocquet +1
A novel method, based on the combination of data assimilation and machine learning is introduced. The new hybrid approach is designed for a two-fold scope: (i) emulating hidden, po…