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Algo-Coop Internal Meetings

The Algo-Coop team meetings are short UNFORMAL seminars of about 20 minutes, dedicated to the introduction of the work done or in progress by the Parallel Algo-Coop team members. They are useful for presenting work you did before arriving at CERFACS, for rehearsing before a conference, for sharing questions on current research with your colleagues…

Beginners are particularly welcome !

We remind you that these meetings are required to be english-spoken.

You can register at this mail address : yzel@cerfacs.fr



  • Implementing block preconditioners for multiphysics problems in firedrake, Ana ORDONEZ
  • Fast Linear Solvers for Compatible Discrete Operator Schemes Arising in Incompressible CFD Simulations, Yongseok JANG
  • Latent Space Data Assimilation by using Deep Learning, Mathis PEYRON
  • Background error covariance matrix estimation from multifidelity ensembles, Jérémy BRIANT & Mayeul DESTOUCHES
  • Fast solvers for robust discretizations in computational fluid dynamics, Pierre MATALON (Thesis defense rehearsal)
  • Parallelisable preconditioners for saddle point weak-constraint 4D-Var, Jemima TABEART (University of Edinburgh)
  • Background error covariance matrix estimation from multifidelity ensembles, Jérémy BRIANT
  • Deep learning for species recognition to help protect bears and an embedded network, Elsa GULLAUD
  • Multilevel Monte Carlo (MLMC) methods, Rob Scheichl (U. Heidelberg)
  • Continuity equations and super-resolution microscopy for the reconstruction of a cell membrane potential, Alfio BORZI (U Wuerzburg)
  • Two recent developments in Multilevel Monte Carlo, Mike GILES (University of Oxford)
  • A multigrid-inspired approach for the Augmented Block Cimmino Distributed solver, Philippe LELEUX
  • Algebraic multigrid preconditioner for statically condensed systems arising from lowest order hybrid discretizations,Pierre MATALON
  • A block minimum residual norm subspace solver for sequences of multiple left and right-hand side linear systems, Yanfei XIANG
  • Accounting for hydrometeor variables in a variational ensemble data assimilation scheme applied to the weather prediction model AROME, Mayeul DESTOUCHES
  • Active learning for LSTM neural networks, Jérémy BRIANT


 Past seminars