The Algo team at Cerfacs develops robust, efficient and scalable numerical methods, and designs their parallel implementation on modern scientific computers. The diversity of the team members’ backgrounds in applied mathematics allows the Algo team to address a wide range of scientific topics. An important and long-standing research area is numerical linear algebra, with a particular emphasis on preconditioning and subspace methods for solving large linear systems using multigrid approaches, randomised techniques and mixed precision computation, to name a few. Data Assimilation (DA) is a core research topic where the team exploits its expertise in numerical linear algebra and scientific computing to develop innovative methods and algorithms for variational DA (weighted regularized nonlinear least squares), ensemble DA and their hybrid combination, with a particular focus on their application to high-dimensional problems arising in Earth system modelling and forecasting. The team’s activities extend to uncertainty quantification in high-dimensional problems, which includes methods for estimating and representing error covariances in DA algorithms, and multilevel and multifidelity methods. The Algo team is also active in scientific machine learning, with contributions to differentiable solvers, AI-based numerical methods, and hybrid DA-AI strategies. Many of these research efforts are conducted in collaboration with the other teams at Cerfacs, but also with research groups in academia and industry at local, national and international level.
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More détails: ALGO Team
Main Industrial Partners : Airbus Group, CEPMMT, CNES, CNRM, EDF, IFPEN, METOFFICE, TOTAL
Academic and Industrial Partners : Argonne National Laboratory (Etats-Unis), CNRS, INPT, INRIA, ISAE, Rutherford Appleton Laboratory (Angleterre), Université de Namur (Belgique), Université de Coimbra (Portugal)
Joint Laboratory : CERFACS-IRIT
National and International Projects : AVENUE (RTRA STAE), ERA-CLIM 2, FILAOS (RTRA STAE), LEFE (INSU-CNRS), EoCoE, PAMSIM, EXCELLERAT, RAISE
