aPC Matlab Toolbox: Data-driven Arbitrary Polynomial Chaos

Data-driven Arbitrary Polynomial Chaos Expansion for Machine Learning, Uncertainty quantification and Global sensitivity analysis

http://www.iws.uni-stuttgart.de

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Cite As

Sergey Oladyshkin (2026). 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. Retrieved .

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.

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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.

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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.

General Information

MATLAB Release Compatibility

  • Compatible with any release

Platform Compatibility

  • Windows
  • macOS
  • Linux
Version Published Release Notes 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

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