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Bertrand Iooss

  1. Derivative-based global sensitivity measures: general links with Sobol' indices and numerical tests.

    Втр, 02/07/2012 - 15:01 — SomNambul
    Authors: Bertrand Iooss, Fabrice Gamboa, Matieyendou Matieyendou, Anne-Laure Popelin
    Subjects: Statistics
    Abstract

    The estimation of variance-based importance measures (called Sobol' indices)
    of the input variables of a numerical model can require a large number of model
    evaluations. It turns to be unacceptable for huge model involving a large
    number of input variables (typically more than ten).

  2. Numerical studies of the metamodel fitting and validation processes.

    Пт, 01/08/2010 - 16:01 — SomNambul
    Authors: Bertrand Iooss, Amandine Marrel, Loïc Boussouf, Vincent Feuillard
    Subjects: Numerical Analysis
    Abstract

    Complex computer codes, for instance simulating physical phenomena, are often
    too time expensive to be directly used to perform uncertainty, sensitivity,
    optimization and robustness analyses. A widely accepted method to circumvent
    this problem consists in replacing cpu time expensive computer models by cpu
    inexpensive mathematical functions, called metamodels. In this paper, we focus
    on the Gaussian process metamodel and two essential steps of its definition
    phase.

  3. Spatial global sensitivity analysis.

    Пнд, 11/09/2009 - 16:01 — SomNambul
    Authors: Bertrand Iooss, Amandine Marrel, Michel Jullien, Beatrice Laurent, Elena Volkova
    Subjects: Computation
    Abstract

    The global sensitivity analysis of a complex numerical model often requires
    the estimation of variance-based importance measures, called Sobol indices.
    Metamodel-based techniques have been developed in order to replace the cpu time
    expensive computer code with an inexpensive mathematical function, predicting
    the computer code output.

  4. Latin hypercube sampling with inequality constraints.

    Чт, 09/03/2009 - 17:38 — SomNambul
    Authors: Matthieu Petelet, Bertrand Iooss, Olivier Asserin, Alexandre Loredo
    Subjects: Computation
    Abstract

    In some studies requiring predictive and CPU-time consuming numerical models,
    the sampling design of the model input variables has to be chosen with caution.
    For this purpose, Latin hypercube sampling has a long history and has shown its
    robustness capabilities. In this paper we propose and discuss a new algorithm
    to build a Latin hypercube sample (LHS) taking into account inequality
    constraints between the sampled variables. This technique, called constrained
    Latin hypercube sampling (cLHS), consists in doing permutations on an initial
    LHS to honor the desired monotonic constraints.

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