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Erschienen in:
2012 | OriginalPaper | Buchkapitel
The Parallel C++ Statistical Library ‘QUESO’: Quantification of Uncertainty for Estimation, Simulation and Optimization
verfasst von : Ernesto E. Prudencio, Karl W. Schulz
Erschienen in: Euro-Par 2011: Parallel Processing Workshops
Verlag: Springer Berlin Heidelberg
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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.