aPC Matlab Toolbox: Data-driven Arbitrary Polynomial Chaos
Citation pour cette source
Sergey Oladyshkin (2024). aPC Matlab Toolbox: Data-driven Arbitrary Polynomial Chaos (https://www.mathworks.com/matlabcentral/fileexchange/72014-apc-matlab-toolbox-data-driven-arbitrary-polynomial-chaos), MATLAB Central File Exchange. Récupéré le .
Oladyshkin, S., and W. Nowak. “Data-Driven Uncertainty Quantification Using the Arbitrary Polynomial Chaos Expansion.” Reliability Engineering & System Safety, vol. 106, Elsevier BV, Oct. 2012, pp. 179–90, doi:10.1016/j.ress.2012.05.002.
Oladyshkin, Sergey, and Wolfgang Nowak. “Incomplete Statistical Information Limits the Utility of High-Order Polynomial Chaos Expansions.” Reliability Engineering & System Safety, vol. 169, Elsevier BV, Jan. 2018, pp. 137–48, doi:10.1016/j.ress.2017.08.010.
Oladyshkin S., de Barros F. P. J. and Nowak W. Global sensitivity analysis: a flexible and efficient framework with an example from stochastic hydrogeology. Advances in Water Resources 37, 10-2, 2012, doi: 10.1016/j.advwatres.2011.11.001.
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- MATLAB > Mathematics > Elementary Math > Polynomials >
- Engineering > Civil and Environmental Engineering > Environmental Engineering >
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aPC Matlab Toolbox
Version | Publié le | Notes de version | |
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1.0.12 | Sparse representation and GP properties |
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1.0.11 | Multivariate Polynomial Degrees |
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1.0.10 | FT Update |
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1.0.9 | PCM Update |
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1.0.8 | OOP |
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1.0.7 | Object Oriented Version |
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1.0.6 | Object Oriented Set Up |
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1.0.5 | Object Oriented Version |
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1.0.4 | Neq1 |
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1.0.3 | Neq1 |
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1.0.2 | New option: aPC based global sensitivity analysis |
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1.0.1 | line 33 in MainRun_aPC.m |
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1.0.0 |