Chaospy: An open source tool for designing methods of uncertainty quantification

•Software for modeling uncertainty using Monte Carlo and polynomial chaos expansions.•Used from Python with a programming syntax close to the mathematical theory.•Can equip forward model with uncertainty quantication results from minimal code.•Highly modular software structure, aimed to serve both e...

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Vydané v:Journal of computational science Ročník 11; s. 46 - 57
Hlavní autori: Feinberg, Jonathan, Langtangen, Hans Petter
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.11.2015
Predmet:
Monte Carlo simulation
Polynomial chaos expansions
Python package
Rosenblatt transformations
Uncertainty quantification
Uncertainty quantification
Polynomial chaos expansions
Rosenblatt transformations
Python package
Monte Carlo simulation
ISSN:1877-7503, 1877-7511
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Popis
Shrnutí:•Software for modeling uncertainty using Monte Carlo and polynomial chaos expansions.•Used from Python with a programming syntax close to the mathematical theory.•Can equip forward model with uncertainty quantication results from minimal code.•Highly modular software structure, aimed to serve both experts and non-experts.•Software compares favorably in functionality with competing packages. The paper describes the philosophy, design, functionality, and usage of the Python software toolbox Chaospy for performing uncertainty quantification via polynomial chaos expansions and Monte Carlo simulation. The paper compares Chaospy to similar packages and demonstrates a stronger focus on defining reusable software building blocks that can easily be assembled to construct new, tailored algorithms for uncertainty quantification. For example, a Chaospy user can in a few lines of high-level computer code define custom distributions, polynomials, integration rules, sampling schemes, and statistical metrics for uncertainty analysis. In addition, the software introduces some novel methodological advances, like a framework for computing Rosenblatt transformations and a new approach for creating polynomial chaos expansions with dependent stochastic variables.
ISSN:1877-7503
1877-7511
DOI:10.1016/j.jocs.2015.08.008