Adaptive Methods Exploring Intrinsic Sparse Structures of Stochastic Partial Differential Equations

Adaptive Methods Exploring Intrinsic Sparse Structures of Stochastic Partial Differential Equations

Many physical and engineering problems involving uncertainty enjoy certain low-dimensional structures, e.g., in the sense of Karhunen-Loeve expansions (KLEs), which in turn indicate the existence of reduced-order models and better formulations for efficient numerical simulations. In this thesis, we...

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Bibliographic Details
Main Author: Cheng, Mulin
Format: Others
Published: 2013
Online Access:https://thesis.library.caltech.edu/7207/1/MulinCheng2013Thesis.pdf
Cheng, Mulin (2013) Adaptive Methods Exploring Intrinsic Sparse Structures of Stochastic Partial Differential Equations. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/V638-V403. https://resolver.caltech.edu/CaltechTHESIS:09182012-175436855 <https://resolver.caltech.edu/CaltechTHESIS:09182012-175436855>

Internet

https://thesis.library.caltech.edu/7207/1/MulinCheng2013Thesis.pdf
Cheng, Mulin (2013) Adaptive Methods Exploring Intrinsic Sparse Structures of Stochastic Partial Differential Equations. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/V638-V403. https://resolver.caltech.edu/CaltechTHESIS:09182012-175436855 <https://resolver.caltech.edu/CaltechTHESIS:09182012-175436855>

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