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  • Workshop info & registration
    workshop will present the main results of the project industry research interactions with real test cases and also and especially the scientific perspectives These will be Sensitivity analysis forcalculation codes Response surface modeling for costly code approximation including intrusive methods such as certified reduced basis Inverse probabilistic modelisation Robust extreme quantile estimation applied to numerical code output See poster Register no need entry is free Location Institut Henri Poincaré amphi

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/workshopsinformationandregistration?tmpl=component&print=1&page= (2016-01-11)
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  • Using R features from Opus
    allowing interaction with an R Python interface called Rpy2 The module converts variables from Opus types to Python types and vice versa The variables are then used by Rpy2 to interact with R This Opus R link is called rpyWrap In essence this allows the Opus user to call R functions with Opus type arguments and receive outputs in Opus types too The conversion of variables is made by the conv function This function will call the overload method of the variable type sent in argument The conv function works both ways As not all variable types have equivalents in the other language an exhaustive list of the possible conversions has been created as of February 2011 There are more complex types to translate such as vector matrix formula and the board of data Note that a Vector s type depends on its contents which are homogenous Consequently there are several vector types vectors of floats integers characters booleans as well as vectors of vectors Type equivalence For more details read this document toto Type conversion does not modify the data so R functions can be called directly with Opus type arguments and return Opus type variables without loss of

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/86-using-r-features-from-opus (2016-01-11)
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  • Workshop info & registration
    The workshop will present the main results of the project industry research interactions with real test cases and also and especially the scientific perspectives These will be Sensitivity analysis forcalculation codes Response surface modeling for costly code approximation including intrusive methods such as certified reduced basis Inverse probabilistic modelisation Robust extreme quantile estimation applied to numerical code output See poster Register no need entry is free Location Institut Henri Poincaré

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/workshopsinformationandregistration?el_mcal_month=12&el_mcal_year=2015 (2016-01-11)
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  • Workshop info & registration
    Program The workshop will present the main results of the project industry research interactions with real test cases and also and especially the scientific perspectives These will be Sensitivity analysis forcalculation codes Response surface modeling for costly code approximation including intrusive methods such as certified reduced basis Inverse probabilistic modelisation Robust extreme quantile estimation applied to numerical code output See poster Register no need entry is free Location Institut Henri

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/workshopsinformationandregistration?el_mcal_month=2&el_mcal_year=2016 (2016-01-11)
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  • Uncertainties-Related Publications
    variances are computed which shows the benefit of an adaptive controlled stratification method This method is finally applied to a real example computation of the peak cladding temperature during a large break loss of coolant accident in a nuclear reactor Adaptive sparse polynomial chaos expansions for uncertainty propagation and sensitivity analysis G Blatman PhD Thesis Université Blaise Pascal Clermont Ferrand II This thesis takes place in the context of uncertainty propagation and sensitivity analysis of computer simulation codes for industrial application It is aimed at carrying out such probabilistic studies while minimizing the number of model evaluations which may reveal time consuming The present work relies upon the expansion of the model response onto the polynomial chaos PC basis which allows the analyst to perform post processing at a negligible cost However fitting the PC expansion may require a high number of calls to the model if the latter depends on a large number of input parameters say more than 10 To circumvent this problem two algorithms are proposed in order to select only a low number of significant terms in the PC approximation namely a stepwise regression scheme and a procedure based on Least Angle Regression LAR Both approaches eventually provide PC representations with a small number of coefficients which may be computed using a reduced number of model evaluations The methods are first tested and compared on various academic examples Then they are applied to the industrial problem of the assessment of a pressure vessel of a nuclear powerplant The obtained results show the efficiency of the proposed procedures to carry out uncertainty and sensitivity analysis of high dimensional problems Quantifying uncertainty in an industrial approach an emerging consensus in an old epistemological debate E de Rocquigny S A P I EN S 2 1 2009 Uncertainty is

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/publications?tmpl=component&print=1&page= (2016-01-11)
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  • Uncertainties-Related Publications
    variances are computed which shows the benefit of an adaptive controlled stratification method This method is finally applied to a real example computation of the peak cladding temperature during a large break loss of coolant accident in a nuclear reactor Adaptive sparse polynomial chaos expansions for uncertainty propagation and sensitivity analysis G Blatman PhD Thesis Université Blaise Pascal Clermont Ferrand II This thesis takes place in the context of uncertainty propagation and sensitivity analysis of computer simulation codes for industrial application It is aimed at carrying out such probabilistic studies while minimizing the number of model evaluations which may reveal time consuming The present work relies upon the expansion of the model response onto the polynomial chaos PC basis which allows the analyst to perform post processing at a negligible cost However fitting the PC expansion may require a high number of calls to the model if the latter depends on a large number of input parameters say more than 10 To circumvent this problem two algorithms are proposed in order to select only a low number of significant terms in the PC approximation namely a stepwise regression scheme and a procedure based on Least Angle Regression LAR Both approaches eventually provide PC representations with a small number of coefficients which may be computed using a reduced number of model evaluations The methods are first tested and compared on various academic examples Then they are applied to the industrial problem of the assessment of a pressure vessel of a nuclear powerplant The obtained results show the efficiency of the proposed procedures to carry out uncertainty and sensitivity analysis of high dimensional problems Quantifying uncertainty in an industrial approach an emerging consensus in an old epistemological debate E de Rocquigny S A P I EN S 2 1 2009 Uncertainty is

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/publications?el_mcal_month=12&el_mcal_year=2015 (2016-01-11)
    Open archived version from archive

  • Uncertainties-Related Publications
    asymptotic variances are computed which shows the benefit of an adaptive controlled stratification method This method is finally applied to a real example computation of the peak cladding temperature during a large break loss of coolant accident in a nuclear reactor Adaptive sparse polynomial chaos expansions for uncertainty propagation and sensitivity analysis G Blatman PhD Thesis Université Blaise Pascal Clermont Ferrand II This thesis takes place in the context of uncertainty propagation and sensitivity analysis of computer simulation codes for industrial application It is aimed at carrying out such probabilistic studies while minimizing the number of model evaluations which may reveal time consuming The present work relies upon the expansion of the model response onto the polynomial chaos PC basis which allows the analyst to perform post processing at a negligible cost However fitting the PC expansion may require a high number of calls to the model if the latter depends on a large number of input parameters say more than 10 To circumvent this problem two algorithms are proposed in order to select only a low number of significant terms in the PC approximation namely a stepwise regression scheme and a procedure based on Least Angle Regression LAR Both approaches eventually provide PC representations with a small number of coefficients which may be computed using a reduced number of model evaluations The methods are first tested and compared on various academic examples Then they are applied to the industrial problem of the assessment of a pressure vessel of a nuclear powerplant The obtained results show the efficiency of the proposed procedures to carry out uncertainty and sensitivity analysis of high dimensional problems Quantifying uncertainty in an industrial approach an emerging consensus in an old epistemological debate E de Rocquigny S A P I EN S 2 1 2009 Uncertainty

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/publications?el_mcal_month=2&el_mcal_year=2016 (2016-01-11)
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  • Formations
    in CFD the scientific society ERCOFTAC European Research Community On Flow Turbulence And Combustion organizes since 2011 a two days awareness course on Uncertainty Management and Quantification in Industrial Analysis and Design This course is specifically intended to CFD communities and it is a good vector to spread the common vision of uncertainty analysis and at the same time getting back new valuable requirements inputs and viewpoints Such courses were held in Germany and USA Virginia Professional training about uncertainty analysis at EDF Through its Institute of Technology Transfer ITech the R D Unit of EDF organizes several training courses which covers the wide range of the company s business areas such as risk management and operating safety scientific computing nuclear energy hydraulics ecology energy markets statistics and data analysis The current ITech training program is made up of 23 courses most take place in EDF R D facilities once or twice a year The courses led by EDF R D engineers and technicians are rooted into the reality of EDF s business and mainly intended to EDF s researchers and engineers However a great number of them are open to participants which are external to the company In particular

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/formations?tmpl=component&print=1&page= (2016-01-11)
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