The parallel c++ statistical library 'QUESO': quantification of uncertainty for estimation, simulation and optimization
Ernesto E. Prudencio,Karl Schulz +1 more
- pp 398-407
TLDR
A collection of statistical algorithms and programming constructs supporting research into the uncertainty quantification of models and their predictions and an easy insertion of new and improved algorithms are described.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.read more
Citations
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Bayesian uncertainty analysis with applications to turbulence modeling
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Remarks on multi-fidelity surrogates
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Bayesian uncertainty quantification applied to RANS turbulence models
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TL;DR: In this paper, a Bayesian uncertainty quantification approach is developed and applied to RANS turbulence models of fully-developed channel flow, which aims to capture uncertainty due to both uncertain parameters and model inadequacy.
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Bayesian calibration, validation, and uncertainty quantification of diffuse interface models of tumor growth
TL;DR: This work attempts to lay out a framework, based on Bayesian probability, for systematically addressing the questions of Validation, the process of investigating the accuracy with which a mathematical model is able to reproduce particular physical events, and Uncertainty quantification, developing measures of the degree of confidence withWhich a computer model predicts particular quantities of interest.
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π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models
Panagiotis E. Hadjidoukas,Panagiotis Angelikopoulos,Costas Papadimitriou,Petros Koumoutsakos +3 more
TL;DR: In this paper, the authors present?4U, an extensible framework for nonintrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures.
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