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The activity on uncertainty quantification at CERFACS aims to estimate uncertainties in computational models requiring large computational resources.  We consider applications related to environmental risk, data assimilation and optimization in a high performance computing context.

The principal actions are:

  • Ensemble experimental design for calibration of uncertain model parameters
  • Development cost-effective Surrogate Model methodologies for computationally intensive problems
  • Sensitivity analysis, optimization and data assimilation
  • Development of efficient algorithms for stochastic parameter estimation

These actions are implemented for the following applications:

  • Surrogates for high dimensional problems in hydraulics and aerodynamics.
  • Efficient, scalable and resilient domain decomposition algorithms for exascale computing
  • Surrogate models for large-eddy simulations of pollution dispersion in the atmospheric boundary layer
  • Developing Multifidelity-MLMC algorithms for industrial CFD, multidisciplinary systems and geosciences.
  • Building simple climate models to explore data constraints on climate projections


Models are approximations of systems.  Confidence in model simulations requires an understanding of their uncertainties.

Uncertainties are generally classified according to their nature. There are several sources of error:

  • errors related to simplifications of equations (dimensional reduction, empirical parameterization)
  • errors related to the numerical schemes
  • errors related to spatio-temporal discretization.

Models must be supplemented by data and parameters that describe the system, initial conditions and its boundary conditions. The physical parameters govern the  laws of the system – and sometimes, these data and parameters are only partially and approximately observed and known.

Errors can be further classified into two groups :

  • Epistemic errors linked to a lack of knowledge of system processes
  • Random errors linked to the stochastic nature of the system

Our aim is to understand and quantify the extent to which epistemic and random uncertainties affect the model response.  At CERFACS, we develop a suite of tools to better understand this propagation of uncertainty.

  • Multi-fidelity Monte-Carlo methods to allow the efficient estimation of probabilistic parameter distributions for industrial and geoscience applications
  • Novel surrogate models to allow rapid exploration of parameter response in global climate and CFD problems.
  • Risk assessment tools bridging scales for climate impacts, connecting global uncertainties to local impacts.


At CERFACS, these activity are transversal, with applications ranging from flood forecasting to wildland fire propagation, climate projection uncertainty and combustion chamber ignition models.


  • EDF
  • IRT
  • CMAP
  • Inria


NextSim General Assembly and TC meeting

CERFACS |  16 September 2021

The General Assembly and TC Meeting took place on 15-16 September 2021. CERFACS is involved in the NextSim project (). The primary objective is to increase the capabilities of Computational Fluid Dynamics tools on extreme-scale parallel computing platforms for aeronautical design. This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement N° 956104. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, France, Germany. This project has received funding from the Agence Nationale de la Recherche (ANR) under grant agreement N° ANR-20-EHPC-0002-02. For more information, please visit Read more

Sophie Valcke from Cerfacs co-authored a new book on atmosphere-ocean modelling

CERFACS |  18 August 2021

new book "Atmosphere-Ocean Modelling - Couling and Couplers” by Prof. Carlos R Mechoso, Prof. Soon-Il An and Dr Sophie Valcke has just been published by World Scientific. The present book fills a void in the current literature by presenting a basic and yet rigorous treatment of how the models of the atmosphere and the ocean are put together into a coupled system. Details are available at  Abstract: Coupled atmosphere-ocean models are at the core of numerical climate models. There is an extraordinarily broad class of coupled atmosphere-ocean models ranging from sets of equations that can be solved analytically to highly detailed representations of Nature requiring the most advanced computers for execution. The models are applied to subjects including the conceptual understanding of Earth’s climate, predictions that support human activities in a variable climate, and projections aimed to prepare society for climate change. The present book fills a void in the current literature by presenting a basic and yet rigorous treatment of how the models of the atmosphere and the ocean are put together into a coupled system. The text of the book is divided into chapters organized according to complexity of the components that are coupled. Two full chapters are dedicated to current efforts on the development of generalist couplers and coupling methodologies all over the worldRead more