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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

CERFACS scientist named in 2019 Clarivate ‘Highly Cited Researchers’ list

superadmin |  25 November 2019

Dr. Rosie Fisher, a researcher at CERFACS working on land surface modeling and terrestrial ecosystem dynamics, was named in the 2019 Web of Science "Highly Cited Researchers" list, which recognizes influential researchers of the past decade, as demonstrated by the authorship of highly-cited papers that rank in the top 1% by citations. Dr Fisher has been working in the Global Change group (GLOBC) at CERFACS since 2018 as part of a collaboration with the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, where she was formerly a staff scientist.Read more


Sparse Days 2020

Brigitte Yzel |  25 November 2019

Sparse Days Meeting 2020 at Cerfacs, Toulouse June 11th-12th, 2020     The annual Sparse Days meeting will be held at CERFACS in Toulouse on 11th and 12th June 2020.  Registration for the Sparse Days is free but we ask people who are coming to register as soon as possible although the deadline is May 10th. Please register using the registration form. Although an emphasis will be on parallel aspects, any talk that has an association with sparsity is welcome. The length for a talk plus questions is 30 minutes although this is negotiable in either direction. Information on accommodation can be found on the web page.Read more

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