The parallel c++ statistical library 'QUESO': quantification of uncertainty for estimation, simulation and optimization

Authors:
Ernesto E. Prudencio;Karl W. Schulz
Affiliations:
Institute for Computational Engineering and Sciences (ICES), The University of Texas, Austin;Institute for Computational Engineering and Sciences (ICES), The University of Texas, Austin
Venue:
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing
Year:
2011

Citing 3
Cited 2

DRAM: Efficient adaptive MCMC

Statistics and Computing
A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

SIAM Journal on Numerical Analysis
Parallel Multilevel Restricted Schwarz Preconditioners with Pollution Removing for PDE-Constrained Optimization

SIAM Journal on Scientific Computing

Letter to the Editor: Probabilistic models and uncertainty quantification for the ionization reaction rate of atomic Nitrogen

Journal of Computational Physics
Data partition methodology for validation of predictive models

Computers & Mathematics with Applications

Quantified Score

Hi-index	0.00

Visualization

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.