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  Space and Chaos-Expansion Galerkin Proper Orthogonal Decomposition Low-order Discretization of PDEs for Uncertainty Quantification

Benner, P., & Heiland, J. (2023). Space and Chaos-Expansion Galerkin Proper Orthogonal Decomposition Low-order Discretization of PDEs for Uncertainty Quantification. International Journal for Numerical Methods in Engineering, 124(12), 2801-2817. doi:10.1002/nme.7229.

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2009.01055.pdf (Preprint), 739KB
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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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 Creators:
Benner, Peter1, Author           
Heiland, Jan1, 2, Author           
Affiliations:
1Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738141              
2Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              

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 Dates: 2020-09-022023
 Publication Status: Issued
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 Rev. Type: Peer
 Identifiers: arXiv: 2009.01055
URI: http://arxiv.org/abs/2009.01055
DOI: 10.1002/nme.7229
Other: pubdata_escidoc:3250919
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Title: International Journal for Numerical Methods in Engineering
Source Genre: Journal
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Pages: - Volume / Issue: 124 (12) Sequence Number: - Start / End Page: 2801 - 2817 Identifier: -