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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 org web

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Original URL path: http://www.opus-project.fr/index.php/component/mailto/?tmpl=component&link=aHR0cDovL3d3dy5vcHVzLXByb2plY3QuZnIvaW5kZXgucGhwL2Fyb3VuZG9wdXMvd29ya3Nob3ByZXBvcnRz (2016-01-11)

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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=12&el_mcal_year=2015 (2016-01-11)

Open archived version from archive - 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=2&el_mcal_year=2016 (2016-01-11)

Open archived version from archive - Workshop reports

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 org web

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

Open archived version from archive - 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/workshopreports?el_mcal_month=12&el_mcal_year=2015 (2016-01-11)

Open archived version from archive - Workshop reports

use 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

Original URL path: http://www.opus-project.fr/index.php/aroundopus/workshopreports?el_mcal_month=2&el_mcal_year=2016 (2016-01-11)

Open archived version from archive - 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/index.php (2016-01-11)

Open archived version from archive