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    November 2009 21 10 Author Administrator What is Opus The aim of OPUS Open source Platform for Uncertainty treatment in Simulation project is to create and sustain an activity around Generic Uncertainty Treatments by building and maintaining a reference tool for uncertainty treatment as well as providing a community friendly environment and extensive user contribution opportunities OPUS gathers 10 industrial and research partners and receives governmental funding via Agence Nationale pour la Recherche More Contributing to Opus Another goal of the project is the building of an active community centered around a forum and user made contributions written for the platform More Download contributions OPUS Final Report The final project report is available for download OPUS Final workshop Computer experiments and uncertainty analysis Friday October 21 2011 Henri Poincaré Institute 11 rue Pierre et Marie Curie Paris Click here for more details in French News Planning New publications Downloads Organisation Planning The workshop Calcul haute performance environnements de calcul et logiciels applications à la quantification d incertitudes will be held on 2011 03 22 in Grenoble in the laboratory Jean Kuntzmann New publications News publications are on line Using R features from Opus and Using Nisp from Opus Downloads Some

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  • ANR project Opus
    become the work environment for the specialist of the treatment of uncertainties Targeted users are once again industrial practitioners those who identify the treatment of uncertainties as a full task which can be spread to multiple engineering domains a python module with high level operators in the probabilistic and statistical field The interest of this language is to be both a powerful scientific language capable of using C libraries and a user friendly interpreted language like Matlab s one This python module was designed to make the development of prototypes easier in order to test new algorithms and methods for example to become an easy to use support for educational works This module intends to become a natural environment capable of integrating new developments within the field of uncertainty and sensitivity analysis The targeted users are here research centres and the academic community Website Last Updated Monday 11 January 2010 22 39 R Monday 04 January 2010 02 48 Author Administrator R R is a language and environment for statistical computing and graphics It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories formerly AT T now Lucent Technologies by John Chambers and colleagues R can be considered as a different implementation of S There are some important differences but much code written for S runs unaltered under R R provides a wide variety of statistical linear and nonlinear modelling classical statistical tests time series analysis classification clustering and graphical techniques and is highly extensible The S language is often the vehicle of choice for research in statistical methodology and R provides an Open Source route to participation in that activity One of R s strengths is the ease with which well designed publication quality plots can be produced including mathematical symbols and formulae where needed Great care has been taken over the defaults for the minor design choices in graphics but the user retains full control R is available as Free Software under the terms of the Free Software Foundation s GNU General Public License in source code form It compiles and runs on a wide variety of UNIX platforms and similar systems including FreeBSD and Linux Windows and MacOS The R environment R is an integrated suite of software facilities for data manipulation calculation and graphical display It includes an effective data handling and storage facility a suite of operators for calculations on arrays in particular matrices a large coherent integrated collection of intermediate tools for data analysis graphical facilities for data analysis and display either on screen or on hardcopy and a well developed simple and effective programming language which includes conditionals loops user defined recursive functions and input and output facilities The term environment is intended to characterize it as a fully planned and coherent system rather than an incremental accretion of very specific and inflexible tools as is frequently the case with other data analysis software R like S is designed around a true computer

    Original URL path: http://www.opus-project.fr/index.php/anropusproject (2016-01-11)
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  • news
    are on line Using R features from Opus document pdf Using Nisp from Opus document pdf 2010 01 14 Annual review The ANR annual review will be held on 2010 04 09 See the private website for details 2009 12 22 Plenary meeting A plenary meeting will be held on Jan 11 2010 See the private website for details 2009 11 22 3rd workshop On the25th of November within Opus project a workshop titled Spectral methods and polynomial chaos will take place at EADS in Suresnes Both theoretical aspects and software implementation will be discussed on the morning and afternoon respectively From 9h30 on Olivier Lemaitre LIMSI and Fabio Nobile Politecnico di Milano will give two theoretical talks On the afternoon Géraud Blatman and Thierry Crestaux two PhD students will present their thesis work related to the subject Concerning the software implementation problems Michael Baudin Scilab and Jean Marc Martinez CEA will present their Scilab toolbox NISP Marc Berveiller EDF and Régis Lebrun EADS will then present their developments on functional chaos in OpenTURNS An access plan to EADS Suresnes is available For security reasons related to the host of the workshop it is mandatory to preregister with Jayant Sen

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/news (2016-01-11)
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  • Project goals
    Uncertainty treatment in Simulation project is to create and sustain an activity around Generic Uncertainty Treatments by building and maintaining a reference tool for uncertainty treatment as well as providing a community friendly environment and extensive user contribution opportunities OPUS gathers 10 industrial and research partners and receives governmental funding via Agence Nationale pour la Recherche Project goals Creating and disseminating a reference opensource tool for uncertainty treatment using cutting edge algorithms contributed from the scientific community Capitalisation of French know how within the uncertainty world around this reference tool Creating a lasting dynamic with different academic industrial and buisiness partners From needs to tools the Opus loop User contributions Opus aims at gathering a community as well as encouraging user contributions Such contributions are various in nature from algorithms ideas submitted in the forum to software packages in several available languages or industrial usecases This allows for Uncertainty aware modelling with cutting edge tools Sustainable Dynamic in the Uncertainty Treatment Field Maintain the cutting edge French know how in industry oriented uncertainty research That promotes the Open Source Software for High Performance Modelling and Complex System conception That establishes France as a major hub in the international Industrial Open

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/projectgoals (2016-01-11)
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  • Project presentation
    assessment All this makes generic uncertainty treatment as opposed to domain specific efforts a cornerstone for rapid industrial progress Hence nowadays several major partnerships ESREDA MUCM SAMSI IMdR have launched ambitious works to capitalise on uncertainty treatments into generic guides that would meet virtually any industrial need It aims at designing a common template to be followed as well as specific guidelines to methodology that might best fit the particular case typically according to the data and experts information availability model complexity and so on In order to reach a broad stakeholder community ranging from physicists to company managers with uncertainty awareness and management capacity an essential step the OPUS project target is to create a genuinely generic and comprehensive software platform OPUS capitalises on the most advanced conceptual and guidelines work into an Open Source however industrialised and fully serviced software product OPUS has gathered the specialists from all relevant fields Ecole Centrale de Paris ECP SUPELEC industry oriented uncertainty research INRIA Host of Scilab Consortium Among other INRIA projects and major advances Scilab is a renowned French Open Source Software in scientific computing Joseph Fourier University and Paris VII University applied mathematicians with connections to R and Scilab Octave recognised Open Source Software CEA EADS EDF and Dassault Aviation leading players in building an industrial consensus over the Generic Uncertainty Treatment Guidelines for industrial use ESREDA EHPOC SOFTIA French start up company with strong records of providing the large companies with services around Open Source solutions Therefore OPUS project leverages much greater know how and resources than reflected by the project balance sheet The synergy of partner experiences provides OPUS with critical weight to launch a Generic Open Source Uncertainty Treatment Platform and create a sustainable dynamic to ensure its durability OPUS promotes the transparency in the uncertainty

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/projectpresentation (2016-01-11)
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  • Project partners
    partners Sunday 22 November 2009 20 12 Author Administrator The project partners are the following entities Industrial CEA Commissariat à l Energie Atomique Dassault Aviation EADS European Aeronautic Defence and Space Company EDF Electricité de France University ECP Ecole Centrale Paris INRIA Institut national de recherche en informatique et en automatique SUPELEC École supérieure d électricité UJF Université Joseph Fourier UP7 Université Paris 7 Small business Softia Prev Next Last

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/projectpartners (2016-01-11)
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  • Project references
    only later on Hence nowadays several major partnerships at the international level ESREDA MUCM SAMSI or the French level IMdR have launched ambitious works to capitalise on uncertainty treatments into generic guides that would meet virtually any industrial need It aims at designing a common template to be followed as well as specific guidelines to methodology that might best fit the particular case typically according to the data and experts information availability model complexity and so on The genuine study cases considered in these projects all from different business areas illustrate a startling feature although the applications and physics involved are completely different the implicit mathematical structure seems very similar Moreover the methodology collections to shop in for each step are common and the choice is rather dependent on the criteria the underlying institutional regulation or study purpose than on the application Indeed the conceptual framework that fits conceptually at least virtually any industrial uncertainty study can be resumed by the following figure This scheme ought to be considered as a skeleton for a purely uncertainty part of an industrial study So far all the three essential steps as well as satellites B and C may be shown application free it is rather mutually dependent At a conceptual level it may be one size fits all solution meanwhile it is not assumed to be so when applied Instead the reference guide provides the methodologies to shop from and the guidelines to chose from that handleads and educates an industrial engineer over the uncertainties aspects avoiding him becoming a specialist in this complex field It may therefore be implemented as Software that would perform all the modelling propagation and criteria check and would only ask some relevant questions playing the role of Babel Fish a translator from the industrial world to the mathematical When dependency structure between the steps of this application free framework is closely investigated it appears that one step directs others in many aspects the purpose of the study often a criterion to comply with or a business decision making together with some features of the available model and the study context variables involved deterministic or random or a bit of both Therefore the very sensible approach to start with is to get it from the right end see the box above This conceptual conclusion comes as an essential point for the consolidation process of building the generic uncertainty management framework In brief the consolidation of uncertainty treatment practices aims at a kind of two side split for a generic industrial study The first one would gather all the bits specific to the particular application whereas the second one would collect the uncertainty related bits Ideally the uncertainty slice may prove to be a common to any application or can be managed to become so and might be embedded into a comprehensive Software package This would offer to an application specialist in charge of a whole study the uncertainty management toolkit applicable to his field know how In

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/projectreferences (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 (2016-01-11)
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