aPC Matlab Toolbox: Data-driven Arbitrary Polynomial Chaos
Data-driven Arbitrary Polynomial Chaos Expansion for Machine Learning, Uncertainty quantification and Global sensitivity analysis
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Zitieren als
Sergey Oladyshkin (2026). aPC Matlab Toolbox: Data-driven Arbitrary Polynomial Chaos (https://de.mathworks.com/matlabcentral/fileexchange/72014-apc-matlab-toolbox-data-driven-arbitrary-polynomial-chaos), MATLAB Central File Exchange. Abgerufen .
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.
Allgemeine Informationen
- Version (92,7 KB)
Kompatibilität der MATLAB-Version
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| Version | Veröffentlicht | Versionshinweise | Action |
|---|---|---|---|
| 1.2.0 | Sparse Bayesian Learning via Automatic Relevance Determination |
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| 1.1.0 | Basis Computation Without a MEX File |
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| 1.0.16 | Sparse Bayesian Learning via Automatic Relevance Determination |
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| 1.0.15 | version 2024a |
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| 1.0.14 | Lasso or Elastic regularization |
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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 |
