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Journal Article

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

MPS-Authors
/persons/resource/persons86253

Benner,  Peter
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

/persons/resource/persons135968

Heiland,  Jan
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

External Resource

https://doi.org/10.5281/zenodo.4005724
(Research data)

https://github.com/mpimd-csc/multidim-genpod-uq
(Research data)

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Fulltext (public)

2009.01055.pdf
(Preprint), 739KB

heiland_3250919.pdf
(Publisher version), 5MB

Supplementary Material (public)
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Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-0006-F342-D
Abstract
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