Cerfacs Enter the world of high performance ...

Uncertainties

Context

➤ Uncertainty quantification has now become a mandatory step in numerical sciences and modeling in a context where computational sciences are constantly increasing.

➤ The main idea is to quantify the uncertainty in the model outputs (called quantities of interest) that are due to uncertainties of various types in the model (aleatory and epistemic errors). These errors relate for instance to model parameters, initial conditions, boundary conditions or model equations.

➤ The classical methods for uncertainty quantification are based on the Monte-Carlo approach that allows to estimate the probability density function (pdf) of quantity of interest with respect to the pdf of the input variables that are considered as aleatory variables. These methods are computationally expensive as they imply a considerable number of model integrations. Numerous alternative methods (intrusive or non-intrusive) aim at building a surrogate model using a limited number of model integrations, which statistics and surface response can be estimated at a limited cost.

feux_UQ1

Response surface of the polynomial decomposition surrogate model for the fire front poropagation model with respect to the vegetation characterictics (humidity and surface/volume ratio). The quantity of interest is the (x-y) position of a marker on the fire line. PhD M. Rochoux, 2014.

➤ This activity is transversal to the following transversal axis at CERFACS Assimilation de données and research axis Environnement , Climat, Aérodynamique and Combustion. The applications at CERFACS relate to flood forecasting, wildland fire propagation, climate variability or combustion chamber ignition.

Objectives

➤ Uncertainty quantification methods can be associated to ensemble based data assimilation algorithms. The identification of uncertainty sources and the quantification of this uncertainty on the quantities of interest formulated in the observation space allow to specify the control vector for the data assimilation algorithm. Additionnaly, these algorithms rely on a stochastic estimate of the errors statistics, that can be estimated at a limited computational cost using a surrogate model. The link between uncertainty quantification and data assimilation is established for the following applications: hydraulique and feux de forêt.  A surrogate model based on a Polynomial Chaos expansion is used in place of the forward model, in order to limit the cost of the ensemble generation within the data assimilation algorithm.

UQ_hydrau

Coefficients of the Polynomial chaos decomposition for the simulated water depth with MASCARET (1D hydraulic model) with respect to uncertainty friction cooefficients and upstream discharge. PhD N. El Mocayd.

➤ A platform dedicated to uncertainty quantification is under development in the framework of the dynamical coupling software OpenPALM using the OpenTURNS (EDF/EADS/Phimeca) library. This preliminary tool offers classical non-intrusive approachs for small to medium dimension problems involving a limited number of model integrations. It is designed for straigthforward use for applicative simple cases that are of interest for CERFACS and its shareholders.

➤ The development of advanced methods adapted to large dimension problems remain an open research field and a challenge for geosciences, combustion and aerodynamics that CERFACS is willing to tackle with.

Partenaires

EDF/LNHE/LHSV UPMC SCHAPI UMD

Projects

  • TOSCA CNES – PhD N. El Mocayd (co-funding EDF)
  • Contract SCHAPI
  • PhD CERFACS (Oct. 2016-2019)

NEWS

Invited communication to the BIDS'17 conference

thual |  12 December 2017

During an invited communication to the « Big Data from Space 2017 » conference, CERFACS presented the stakes and the perspectives of its strategic axis "Data Driven Modeling" from the point of view of the valorization of satellite data. For more details, see the article:...Read more


One Cerfacs’ proposal accepted in PRACE 15th call

superadmin |  4 December 2017

This study focuses on Hall-effect thrusters, which were first invented in the 1960s. Although such systems have been extensively studied, the detailed physics of the magnetized plasmas in these thrusters is very complex and several plasma processes that have direct influence on the thruster...Read more

ALL NEWS