DaPC NN: Deep Arbitrary Polynomial Chaos Neural Network
Zitieren als
Sergey Oladyshkin (2024). DaPC NN: Deep Arbitrary Polynomial Chaos Neural Network (https://www.mathworks.com/matlabcentral/fileexchange/112110-dapc-nn-deep-arbitrary-polynomial-chaos-neural-network), MATLAB Central File Exchange. Abgerufen .
Oladyshkin, S., and W. Nowak. “Data-Driven Uncertainty Quantification Using the Arbitrary Polynomial Chaos Expansion.” Reliability Engineering &Amp\Mathsemicolon 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 &Amp\Mathsemicolon System Safety, vol. 169, Elsevier BV, Jan. 2018, pp. 137–48, doi:10.1016/j.ress.2017.08.010.
Oladyshkin S., Praditia T., Kroeker I., Mohammadi F., Nowak W., Otte S., The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory. Neural Networks. Elsevier, 2023. DOI: 10.1016/j.neunet.2023.06.036.
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DaPC NN Matlab Toolbox
Version | Veröffentlicht | Versionshinweise | |
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0.0.4 | Multivariate Polynomial Degrees |
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0.0.3 | Alpha Version 0.0.3 |
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0.0.2 | Alpha Version 0.0.2 |
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0.0.1 |