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    The Parallel C++ Statistical Library ‘QUESO’: Quantification of Uncertainty for Estimation, Simulation and Optimization

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    Type
    Book Chapter
    Authors
    Prudencio, Ernesto E.
    Schulz, Karl W.
    Date
    2012
    Permanent link to this record
    http://hdl.handle.net/10754/599945
    
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    Abstract
    QUESO is a collection of statistical algorithms and programming constructs supporting research into the uncertainty quantification (UQ) of models and their predictions. It has been designed with three objectives: it should (a) be sufficiently abstract in order to handle a large spectrum of models, (b) be algorithmically extensible, allowing an easy insertion of new and improved algorithms, and (c) take advantage of parallel computing, in order to handle realistic models. Such objectives demand a combination of an object-oriented design with robust software engineering practices. QUESO is written in C++, uses MPI, and leverages libraries already available to the scientific community. We describe some UQ concepts, present QUESO, and list planned enhancements.
    Citation
    Prudencio EE, Schulz KW (2012) The Parallel C++ Statistical Library “QUESO”: Quantification of Uncertainty for Estimation, Simulation and Optimization. Lecture Notes in Computer Science: 398–407. Available: http://dx.doi.org/10.1007/978-3-642-29737-3_44.
    Sponsors
    This work has been supported by the National Nuclear Se-curity Administration, U.S D.O.E., under Award Number DE-FC52-08NA28615.The first author was also partially supported by Sandia National Laborato-ries, under Contracts1017123 and 1086312, and by King Abdullah University of Science and Technology (KAUST), under the Academic Excellence Allianceprogram. The authors would also like to thank fruitful discussions with manyresearchers, including P. Bauman, S. H. Cheung, M. Eldred, O. Ghattas, J.Martin, K. Miki, F. Nobile, T. Oliver, C. Simmons, L. Swiler, R. Tempone, G.Terejanu and L. Wilcox.
    Publisher
    Springer Nature
    Journal
    Euro-Par 2011: Parallel Processing Workshops
    DOI
    10.1007/978-3-642-29737-3_44
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-29737-3_44
    Scopus Count
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