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    2009 2009 OpenTURNS an Open Source initiative to Treat Uncertainties Risks N Statistics in a structured industrial approach Dutfoy A Dutka Malen I Pasanisi A Lebrun R Mangeant F Sen Gupta J Pendola M Yalamas T 41èmes Journées de Statistique Bordeaux mai 2009 Software Using R features from Opus This document aims at presenting the Opus R link module It will allow calling R features directly from Opus The module was developed in Python so as to allow use of the Opus TUI It acts as a wrapper 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 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 information From a practical point of view using the wrapper is simple as

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/results?el_mcal_month=11&el_mcal_year=2015 (2016-01-11)
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  • Results
    2009 OpenTURNS an Open Source initiative to Treat Uncertainties Risks N Statistics in a structured industrial approach Dutfoy A Dutka Malen I Pasanisi A Lebrun R Mangeant F Sen Gupta J Pendola M Yalamas T 41èmes Journées de Statistique Bordeaux mai 2009 Software Using R features from Opus This document aims at presenting the Opus R link module It will allow calling R features directly from Opus The module was developed in Python so as to allow use of the Opus TUI It acts as a wrapper 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 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 information From a practical point of view using the wrapper is simple as a

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/results?el_mcal_month=3&el_mcal_year=2016 (2016-01-11)
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  • Formations
    Deterministic Simulations 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

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/87-formations (2016-01-11)
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  • Uncertainties-Related Publications
    and the 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

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/83-relatedpublications (2016-01-11)
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  • Workshop info & registration
    17h30 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

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/80-workshopsinformationandregistration (2016-01-11)
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  • Workshop reports
    specific dependencies and development environments R Matlab Scilab Octave C We shall discuss of good processes to establish and pitfalls to avoid The service and long term prospects sub session will start right after the coffee break It will address the issues of commercial service around open source free software and the project life after the initial funding dries out In both sub sessions the question of intellectual property copyright and licensing is important and thus shall be discussed although from different point of views Presentation slides Top Workshop 2 2009 04 29 The second OPUS workshop dealing with machine learning and model selection with an emphasis on DICE consortium s work took place on the 29th of April 2009 at the CEA in Saclay Continuing the formula of two half days from the first workshop the morning was spent on three theoretical presentations R Gramacy F Bach L Carraro while the afternoon was spent as a round table dealing with software integration of contributions in a single free platform The first returns are positive However improvement is possible In the next workshop it seems mandatory to introduce the Opus project as well as its aims and ways to contribute Indeed these elements will facilitate comprehension of the context as wall as constitute a basis for the talks of the afternoon Workshop program Professor Robert B Gramacy Statistical Laboratory Department of Mathematics University of Cambridge UK Bayesian treed Gaussian process models Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response However such sweeps are unnecessary in regions where the response is easily predicted well chosen designs could allow a mapping of the response with far fewer simulation runs I explore a modern approach that couples two standard regression models Gaussian processes and treed partitioning A Bayesian perspective yields an explicit measure of nonstationary predictive uncertainty that can be used to guide sampling The methods will be illustrated through several examples including a motivating example which involves the computational fluid dynamics of a NASA re entry vehicle Related documents Slides Figures A daptive Design and Analysis of Supercomputer Experiments 2009 with Herbert K H Lee Technometrics 51 2 pp 130 145 preprint on arXiv 0805 4359 Bayesian treed Gaussian process models with an application to computer modeling 2008 with Herbert K H Lee Journal of the American Statistical Association 103 483 pp 1119 1130 preprint on arXiv 0710 4536 tgp An R Package for Bayesian Nonstationary Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models 2007 Journal of Statistical Software 19 9 snapshot of the R vignette for the tgp package as of June 2007 Categorical inputs sensitivity analysis optimization and importance tempering with tgp version 2 an R package for treed Gaussian process models 2009 with Matt Taddy To appear in the Journal of Statistical Software snapshot of one of two R vignettes in the tgp package as of January 2010 The tgp package on cran http www cran r project

    Original URL path: http://www.opus-project.fr/index.php/aroundopus/35-workshopreports (2016-01-11)
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  • Around Opus
    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 Hermite 11 rue Pierre et Marie Curie Paris 5ème Free entry Last Updated Wednesday 05 October 2011 20 11 Workshop reports Sunday 22 November 2009 20 14 Author Administrator Workshops Workshop 1 2008 10 08 Professor Tony Patera MIT USA An overview on reduced basis error estimation and applications to uncertainty quantification Professor Kai Tai Fang Beijing Normal University China Design and Modeling for experiments with model uncertainty Emmanuel Vazquez Supelec France On kriging and sequential search algorithms Round table with R Gentleman from R and D Bateman from Octave Workshop 2 2009 04 29 Professor Robert B Gramacy Statistical Laboratory Department of Mathematics University of Cambridge UK Bayesian treed Gaussian process models Professor Laurent Carraro Département 3MI Ecole des mines de Saint Etienne France Scientific contributions in the DICE consortium Professor Francis Bach Département d Informatique Ecole Normale Supérieure France Multiple kernel learning Round table software integration of contributions in a single free platform Workshop 3 2009 10 25 Spectral methods and polynomial chaos Olivier Lemaitre LIMSI Fabio Nobile Politecnico di Milano Sparse Grid Stochastic Collocation methods for Uncertainty Quantifi cation Géraud Blatman and Thierry Crestaux thesis work Michael Baudin Scilab and Jean Marc Martinez CEA Scilab toolbox NISP Marc Berveiller EDF and Régis Lebrun EADS developments on functional chaos in OpenTURNS Workshop 4 2010 06 29 Uncertainty propagation rare quantile and extreme failure proability estimation Alberto Pasanisi EDF R D Introduction Josselin Garnier Université Paris 7 Interacting particle systems for the analysis of rare events Philippe Naveau Lab Sciences du Climat et l Environnement CNRS Applications of multivariate extreme value theory to environmental data analysis Pierre Del Moral INRIA Université Bordeaux 1 Sur les interprétations particulaires d événements rares On rare events particular interpretation Régis Lebrun EADS IW Algorithmes de simulation en espace standard Simulation algorithms in standard space Bruno Sudret Phiméca Méta modèles pour le calcul de probabilités d événements rares Metamodels for rare event probability calculation Fabien Mangeant EADS IW Calcul de quantiles faibles pour une application de guidage Small quantile calculation for a guiding application Miguel Munoz Zuniga EDF R D Université Paris 7 Estimation de faibles probabilités de défaillance par une méthode originale de Monte Carlo accélérée Extreme failure probability estimation by an original accelerated Monte Carlo method Alberto Pasanisi Conclusion Workshop 5 2011 03 22 Calcul haute performance environnements de calcul et logiciels applications à la quantification d incertitudes C Perez INRIA Tendances dans le calcul haute performance C Prieur UJF L Viry UJF B Depardon Sysfera Analyse de sensibilité pour la mousson en Afrique de l ouest de la méthodologie au calcul distribué D Busby IFP Energies Nouvelles Cougar F Gaudier CEA Uranie R Barate EDF I Dutka Malen EDF P Benjamin EADS OpenTURNS Workshop 1 2008 10 08 The first OPUS workshop took place at EDF Clamart near Paris on October 8 2008 This workshop was organized by Guennadi Andrianov from EDF and Anestis Antoniadis and Christophe Prud homme both from UJF LJK Three talks were organized in the morning Professor Tony Patera MIT USA An overview on reduced basis error estimation and applications to uncertainty quantification We discuss reduced basis approximation and associated a posteriori error estimation for reliable real time solution of parametrized partial differential equations The crucial ingredients are rapidly convergent Galerkin approximations over a space spanned by snapshots on the parametrically induced solution manifold rigorous and sharp a posteriori error estimators for the outputs quantities of interest effective constructions for stability constant lower bounds efficient Greedy in parameter or POD in time Greedy in parameter selection of quasi optimal samples and Offine Online computational procedures for rapid calculation in the many query and real time contexts We consider linear and nonlinear elliptic problems linear and nonlinear parabolic equations and linear hyperbolic equations Examples are drawn from heat transfer steady and unsteady conduction and convection acoustics in the frequency and time domains solid mechanics e g crack stress intensity factors and fluid dynamics the incompressible Navier Stokes equations Finally we discuss the application of our reduced basis approximations and error bounds to uncertainty analysis We consider two contexts both of which exploit the many query efficiency and reliability of the reduced basis formulation In the first forward context we explore output variation in the presence of stochastic parameter dependence In the second inverse context we address parameter estimation in the presence of numerical and experimental output error In both cases we realize computational savings of several orders of magnitude relative to classical approaches Presentation slides More information papers talks software at http augustine mit edu Top Professor Kai Tai Fang Beijing Normal University China Design and Modeling for experiments with model uncertainty Presentation slides Top Emmanuel Vazquez Supelec France On kriging and sequential search algorithms The optimization of functions whose evaluation involves time consuming computer programs has often to be achieved with a small budget of evaluations In this context the Expected Improvement EI algorithm a kriging based approach to optimization has become popular for it can lead to significant savings in the number of function evaluations over traditional optmization methods The EI algorithm is known as a sequential Bayesian global optimization technique During the optimization the expensive to evaluate function is replaced by a cheap approximation and the probabilistic framework of kriging is used to account for the uncertainty on the function approximation This talk will start with a presentation of the technique and will discuss several variations including the recently proposed Informational Approach to Global Optimization IAGO strategy which takes a step forward in this domain and has successfully been applied in the context of industrial problems Finally we will show that the ideas supporting Bayesian optimization can be generalized to derive other types of

    Original URL path: http://www.opus-project.fr/index.php/aroundopus?el_mcal_month=11&el_mcal_year=2015 (2016-01-11)
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  • Around Opus
    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 Hermite 11 rue Pierre et Marie Curie Paris 5ème Free entry Last Updated Wednesday 05 October 2011 20 11 Workshop reports Sunday 22 November 2009 20 14 Author Administrator Workshops Workshop 1 2008 10 08 Professor Tony Patera MIT USA An overview on reduced basis error estimation and applications to uncertainty quantification Professor Kai Tai Fang Beijing Normal University China Design and Modeling for experiments with model uncertainty Emmanuel Vazquez Supelec France On kriging and sequential search algorithms Round table with R Gentleman from R and D Bateman from Octave Workshop 2 2009 04 29 Professor Robert B Gramacy Statistical Laboratory Department of Mathematics University of Cambridge UK Bayesian treed Gaussian process models Professor Laurent Carraro Département 3MI Ecole des mines de Saint Etienne France Scientific contributions in the DICE consortium Professor Francis Bach Département d Informatique Ecole Normale Supérieure France Multiple kernel learning Round table software integration of contributions in a single free platform Workshop 3 2009 10 25 Spectral methods and polynomial chaos Olivier Lemaitre LIMSI Fabio Nobile Politecnico di Milano Sparse Grid Stochastic Collocation methods for Uncertainty Quantifi cation Géraud Blatman and Thierry Crestaux thesis work Michael Baudin Scilab and Jean Marc Martinez CEA Scilab toolbox NISP Marc Berveiller EDF and Régis Lebrun EADS developments on functional chaos in OpenTURNS Workshop 4 2010 06 29 Uncertainty propagation rare quantile and extreme failure proability estimation Alberto Pasanisi EDF R D Introduction Josselin Garnier Université Paris 7 Interacting particle systems for the analysis of rare events Philippe Naveau Lab Sciences du Climat et l Environnement CNRS Applications of multivariate extreme value theory to environmental data analysis Pierre Del Moral INRIA Université Bordeaux 1 Sur les interprétations particulaires d événements rares On rare events particular interpretation Régis Lebrun EADS IW Algorithmes de simulation en espace standard Simulation algorithms in standard space Bruno Sudret Phiméca Méta modèles pour le calcul de probabilités d événements rares Metamodels for rare event probability calculation Fabien Mangeant EADS IW Calcul de quantiles faibles pour une application de guidage Small quantile calculation for a guiding application Miguel Munoz Zuniga EDF R D Université Paris 7 Estimation de faibles probabilités de défaillance par une méthode originale de Monte Carlo accélérée Extreme failure probability estimation by an original accelerated Monte Carlo method Alberto Pasanisi Conclusion Workshop 5 2011 03 22 Calcul haute performance environnements de calcul et logiciels applications à la quantification d incertitudes C Perez INRIA Tendances dans le calcul haute performance C Prieur UJF L Viry UJF B Depardon Sysfera Analyse de sensibilité pour la mousson en Afrique de l ouest de la méthodologie au calcul distribué D Busby IFP Energies Nouvelles Cougar F Gaudier CEA Uranie R Barate EDF I Dutka Malen EDF P Benjamin EADS OpenTURNS Workshop 1 2008 10 08 The first OPUS workshop took place at EDF Clamart near Paris on October 8 2008 This workshop was organized by Guennadi Andrianov from EDF and Anestis Antoniadis and Christophe Prud homme both from UJF LJK Three talks were organized in the morning Professor Tony Patera MIT USA An overview on reduced basis error estimation and applications to uncertainty quantification We discuss reduced basis approximation and associated a posteriori error estimation for reliable real time solution of parametrized partial differential equations The crucial ingredients are rapidly convergent Galerkin approximations over a space spanned by snapshots on the parametrically induced solution manifold rigorous and sharp a posteriori error estimators for the outputs quantities of interest effective constructions for stability constant lower bounds efficient Greedy in parameter or POD in time Greedy in parameter selection of quasi optimal samples and Offine Online computational procedures for rapid calculation in the many query and real time contexts We consider linear and nonlinear elliptic problems linear and nonlinear parabolic equations and linear hyperbolic equations Examples are drawn from heat transfer steady and unsteady conduction and convection acoustics in the frequency and time domains solid mechanics e g crack stress intensity factors and fluid dynamics the incompressible Navier Stokes equations Finally we discuss the application of our reduced basis approximations and error bounds to uncertainty analysis We consider two contexts both of which exploit the many query efficiency and reliability of the reduced basis formulation In the first forward context we explore output variation in the presence of stochastic parameter dependence In the second inverse context we address parameter estimation in the presence of numerical and experimental output error In both cases we realize computational savings of several orders of magnitude relative to classical approaches Presentation slides More information papers talks software at http augustine mit edu Top Professor Kai Tai Fang Beijing Normal University China Design and Modeling for experiments with model uncertainty Presentation slides Top Emmanuel Vazquez Supelec France On kriging and sequential search algorithms The optimization of functions whose evaluation involves time consuming computer programs has often to be achieved with a small budget of evaluations In this context the Expected Improvement EI algorithm a kriging based approach to optimization has become popular for it can lead to significant savings in the number of function evaluations over traditional optmization methods The EI algorithm is known as a sequential Bayesian global optimization technique During the optimization the expensive to evaluate function is replaced by a cheap approximation and the probabilistic framework of kriging is used to account for the uncertainty on the function approximation This talk will start with a presentation of the technique and will discuss several variations including the recently proposed Informational Approach to Global Optimization IAGO strategy which takes a step forward in this domain and has successfully been applied in the context of industrial problems Finally we will show that the ideas supporting Bayesian optimization can be generalized to derive other types of sequential

    Original URL path: http://www.opus-project.fr/index.php/aroundopus?el_mcal_month=1&el_mcal_year=2016 (2016-01-11)
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