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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

Medal of the Academy of Air and Space: 3 CERFACS researchers awarded !

Brigitte Yzel |  11 July 2022

The Air and Space Academy awarded Carlos Pérez Arroyo, Gabriel Staffelbach and Jérôme Dombard with the Vermeil medal 2022, to honor the excellence of their work in the realization of the FULLEST project, the first high-fidelity simulation of an aircraft engine. Thanks to PRACE for awarding the access to the Joliot-Curie supercomputer (GENCI hosted at CEA/TGCC) and to DGAC for the funding of the ATOM project (No 2018-39) led by SAFRAN Tech. FULLEST also benefited from developments done in European projects:  EXCELLERAT (H2020 823691) and EPEEC (H2020 801051).  Read more


ROLAND GLOWINSKI CONFERENCE IN PARIS FROM JULY 5th to 7th

CERFACS |  4 July 2022

CERFACS will participate to the Paris meeting dedicated to the memory of Roland Glowinski from July 5th to 7th. Thierry Poinsot will present the advances of HPC in the field of energy and combustion. Roland Glowinski was CERFACS direcfor for 3 years in the 90s and largely shaped present activities of our research center.Read more

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