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  Pygpc: A sensitivity and uncertainty analysis toolbox for Python

Weise, K., Poßner, L., Müller, E., Gast, R., & Knösche, T. R. (2020). Pygpc: A sensitivity and uncertainty analysis toolbox for Python. SoftwareX, 11: 100450. doi:10.1016/j.softx.2020.100450.

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Weise_2020.pdf (Publisher version), 2MB
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 Creators:
Weise, Konstantin1, 2, Author              
Poßner, Lucas3, Author
Müller, Erik3, Author
Gast, Richard1, Author              
Knösche, Thomas R.1, 4, Author              
Affiliations:
1Methods and Development Group Brain Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205650              
2Department of Advanced Electromagnetics, TU Ilmenau, Germany, ou_persistent22              
3Faculty of Electrical Engineering and Information Technology, University of Applied Sciences, Leipzig, Germany, ou_persistent22              
4Institute for Biomedical Engineering and Informatics, TU Ilmenau, Germany, ou_persistent22              

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Free keywords: Sensitivity analysis; Uncertainty analysis; Polynomial chaos
 Abstract: We present a novel Python package for the uncertainty and sensitivity analysis of computational models. The mathematical background is based on the non-intrusive generalized polynomial chaos method allowing one to treat the investigated models as black box systems, without interfering with their legacy code. Pygpc is optimized to analyze models with complex and possibly discontinuous transfer functions that are computationally costly to evaluate. The toolbox determines the uncertainty of multiple quantities of interest in parallel, given the uncertainties of the system parameters and inputs. It also yields gradient-based sensitivity measures and Sobol indices to reveal the relative importance of model parameters.

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Language(s): eng - English
 Dates: 2020-03-062020-01-312020-03-062020-03-13
 Publication Status: Published online
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1016/j.softx.2020.100450
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Project name : -
Grant ID : WE 59851/2
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Funding organization : German Science Foundation (DFG)

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Title: SoftwareX
Source Genre: Journal
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Publ. Info: Elsevier
Pages: - Volume / Issue: 11 Sequence Number: 100450 Start / End Page: - Identifier: ISSN: 2352-7110
CoNE: https://pure.mpg.de/cone/journals/resource/2352-7110