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DDM-Uncertainties

Objectives

The activity on uncertainty quantification at CERFACS aims to estimate uncertainties for numerical models requiring large computational resources. We develop environmental risk assessments, data assimilation approaches, probabilistic optimization and coupling improvements for High Performance Computing applications.

At CERFACS, the activity on uncertainty quantification relates to the transversal axis “Data-Driven Modeling” and is at the crossroads of the Environment, Climate, Aerodynamics and Combustion thematic axes. Applications at CERFACS concern flood forecasting, prediction of atmospheric dispersion of pollutants, wildfire propagation, representation of climatic variability and combustion chamber ignition calculations.

The main actions are :

  • Ensemble-based simulations with scalar and functional variables, including dimension reduction strategy
  • Development and evaluation of reduced models for large scale problems
  • Use of reduced models for sensitivity analysis, optimization and data assimilation
  • Development of efficient algorithms for stochastic estimation with solvers of increasing complexity (multi-fidelity, multi-level Monte Carlo/MLMC)

These actions are deployed on the following applications:

  • Development of uncertainty quantification algorithms for real scale computation with efficient, scalable and robust domain decomposition algorithms
  • Development of reduced models for sensitivity analysis and ensemble data assimilation, application in hydraulics and aerodynamics for large uncertain variables
  • Application of reduced models for atmospheric boundary layer simulations in the context of uncertainty quantification, application to micro-scale meteorology and in particular to pollutant dispersion
  • Application of multi-fidelity and MLMC algorithms for industrial computational fluid mechanics, multidisciplinary systems and geosciences.
  • Parametric sensitivity tests of global climate model projections to assess uncertainties in regional and global climate risks
  • Simple climate models to assess data constraints on global climate projection uncertainty

Collaboration

EDFLISNIRTONERA
AirbusCMAPInriaMétéo-France

NEWS

Aeronautics & Space Awards: Carlos Pérez Arroyo, Jérôme Dombard and Gabriel Staffelbach are awarded the Vermeil medal

Brigitte Yzel |  2 December 2022

  Formal Session 2022 of the Air and Space Academy Friday, December 9, 2:00-6:00 pm   📌 in person at the Salle des Illustres - Mairie de Toulouse  💻 and in visio  ✅ Mandatory registration (face-to-face or visio):   During the formal public session of the Air and Space Academy 2022, which will take place on Friday, December 9, starting at 2:00 pm, Salle des Illustres, Hôtel de Ville de Toulouse, in face-to-face and remote sessions, the Vermeil Medal will be awarded (at 5:15 pm) to Carlos PÉREZ-ARROYO, Jérôme DOMBARD, and Gabriel STAFFELBACH to honor the excellence of their work in the realization of the FULLEST project, the first high-fidelity simulation of an aircraft engine.    Read more


CERFACS cited in l’Usine Nouvelle, Industrie et Technologies

Corentin LAPEYRE |  25 November 2022

The work of CERFACS, in collaboration with SAFRAN, on the hybridization of physical simulation software with machine learning techniques, has been cited in the magazine l'Usine Nouvelle, and is included in a detailed write-up called "Simulation and machine learning combine their strengths" in the magazine Industries et Technologies this month. This work, supervised at CERFACS by Corentin Lapeyre, and at SAFRAN by Stéphane Richard, is at the forefront of developments in terms of exploiting artificial intelligence tools for the simulation and design of physical systems.Read more

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