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Adaptive methods exploring intrinsic sparse structures of stochastic partial differential equations

Citation

Cheng, Mulin (2013) Adaptive methods exploring intrinsic sparse structures of stochastic partial differential equations. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:09182012-175436855

Abstract

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 target a class of time-dependent stochastic partial differential equations whose solutions enjoy such structures at any time and propose a new methodology (DyBO) to derive equivalent systems whose solutions closely follow KL expansions of the original stochastic solutions. KL expansions are known to be the most compact representations of stochastic processes in an $L^2$ sense. Our methods explore such sparsity and offer great computational benefits compared to other popular generic methods, such as traditional Monte Carlo (MC), generalized Polynomial Chaos (gPC) method, and generalized Stochastic Collocation (gSC) method. Such benefits are demonstrated through various numerical examples ranging from spatially one-dimensional examples, such as stochastic Burgers' equations and stochastic transport equations to spatially two-dimensional examples, such as stochastic flows in 2D unit square. Parallelization is also discussed, aiming toward future industrial-scale applications. In addition to numerical examples, theoretical aspects of DyBO are also carefully analyzed, such as preservation of bi-orthogonality, error propagation, and computational complexity. Based on theoretical analysis, strategies are proposed to overcome difficulties in numerical implementations, such as eigenvalue crossing and adaptively adding or removing mode pairs. The effectiveness of the proposed strategies is numerically verified. Generalization to a system of SPDEs is considered as well in the thesis, and its success is demonstrated by applications to stochastic Boussinesq convection problems. Other generalizations, such as generalized stochastic collocation formulation of DyBO method, are also discussed.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Karhunen Loeve Expansion, Stochastic Partial Differential Equation, Low-Dimensional Structure, Reduced-Order Model, Stochastic Flow, Sparsity.
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Applied And Computational Mathematics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Hou, Thomas Y.
Thesis Committee:
  • Hou, Thomas Y. (chair)
  • Owhadi, Houman
  • Meiron, Daniel I.
  • Beck, James L.
Defense Date:12 September 2012
Author Email:mulin.cheng (AT) gmail.com
Funders:
Funding AgencyGrant Number
AFOSR MURIFA9550-09-1-0613
Record Number:CaltechTHESIS:09182012-175436855
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:09182012-175436855
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:7207
Collection:CaltechTHESIS
Deposited By: Mulin Cheng
Deposited On:30 Oct 2012 17:39
Last Modified:07 Oct 2013 16:46

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