QUESO

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


References in zbMATH (referenced in 15 articles )

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  1. Bauman, Paul T.; Stogner, Roy H.: GRINS: a multiphysics framework based on the libMesh finite element library (2016)
  2. Lima, E.A.B.F.; Oden, J.T.; Hormuth, D.A.II; Yankeelov, T.E.; Almeida, R.C.: Selection, calibration, and validation of models of tumor growth (2016)
  3. Lin, Xiao; Terejanu, Gabriel; Shrestha, Sajan; Banerjee, Sourav; Chanda, Anindya: Bayesian model selection framework for identifying growth patterns in filamentous fungi (2016)
  4. Farrell, Kathryn; Oden, J.Tinsley; Faghihi, Danial: A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems (2015)
  5. Hadjidoukas, P.E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.: $\Pi$4U: a high performance computing framework for Bayesian uncertainty quantification of complex models (2015)
  6. Miki, Kenji; Panesi, Marco; Prudhomme, Serge: Systematic validation of non-equilibrium thermochemical models using Bayesian inference (2015)
  7. Prudencio, E.E.; Bauman, P.T.; Faghihi, D.; Ravi-Chandar, K.; Oden, J.T.: A computational framework for dynamic data-driven material damage control, based on Bayesian inference and model selection (2015)
  8. Farrell, Kathryn; Oden, J.Tinsley: Calibration and validation of coarse-grained models of atomic systems: application to semiconductor manufacturing (2014)
  9. Morrison, Rebecca E.; Bryant, Corey M.; Terejanu, Gabriel; Prudhomme, Serge; Miki, Kenji: Data partition methodology for validation of predictive models (2013)
  10. Oden, J.Tinsley; Prudencio, Ernesto E.; Bauman, Paul T.: Virtual model validation of complex multiscale systems: applications to nonlinear elastostatics (2013)
  11. Oden, J.Tinsley; Prudencio, Ernesto E.; Hawkins-Daarud, Andrea: Selection and assessment of phenomenological models of tumor growth (2013)
  12. Miki, K.; Panesi, M.; Prudencio, E.E.; Prudhomme, S.: Probabilistic models and uncertainty quantification for the ionization reaction rate of atomic nitrogen (2012)
  13. Prudencio, Ernesto E.; Cheung, Sai Hung: Parallel adaptive multilevel sampling algorithms for the Bayesian analysis of mathematical models (2012)
  14. Prudencio, Ernesto E.; Schulz, Karl W.: The parallel C++ statistical library `QUESO’: quantification of uncertainty for estimation, simulation and optimization (2012)
  15. Oden, J.Tinsley; Prudhomme, Serge: Control of modeling error in calibration and validation processes for predictive stochastic models (2011)