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

Ernesto E. Prudencio, Karl W. Schulz

Research output: Chapter in Book/Report/Conference proceedingChapter

44 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationEuro-Par 2011: Parallel Processing Workshops
PublisherSpringer Nature
Pages398-407
Number of pages10
ISBN (Print)9783642297366
DOIs
StatePublished - 2012
Externally publishedYes

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