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Multiscale model reduction methods for deterministic and stochastic partial differential equations

Citation

Ci, Maolin (2014) Multiscale model reduction methods for deterministic and stochastic partial differential equations. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:03312014-014047677

Abstract

Partial differential equations (PDEs) with multiscale coefficients are very difficult to solve due to the wide range of scales in the solutions. In the thesis, we propose some efficient numerical methods for both deterministic and stochastic PDEs based on the model reduction technique.

For the deterministic PDEs, the main purpose of our method is to derive an effective equation for the multiscale problem. An essential ingredient is to decompose the harmonic coordinate into a smooth part and a highly oscillatory part of which the magnitude is small. Such a decomposition plays a key role in our construction of the effective equation. We show that the solution to the effective equation is smooth, and could be resolved on a regular coarse mesh grid. Furthermore, we provide error analysis and show that the solution to the effective equation plus a correction term is close to the original multiscale solution.

For the stochastic PDEs, we propose the model reduction based data-driven stochastic method and multilevel Monte Carlo method. In the multiquery, setting and on the assumption that the ratio of the smallest scale and largest scale is not too small, we propose the multiscale data-driven stochastic method. We construct a data-driven stochastic basis and solve the coupled deterministic PDEs to obtain the solutions. For the tougher problems, we propose the multiscale multilevel Monte Carlo method. We apply the multilevel scheme to the effective equations and assemble the stiffness matrices efficiently on each coarse mesh grid. In both methods, the $\KL$ expansion plays an important role in extracting the main parts of some stochastic quantities.

For both the deterministic and stochastic PDEs, numerical results are presented to demonstrate the accuracy and robustness of the methods. We also show the computational time cost reduction in the numerical examples.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:model reduction; multiscale; PDE; SPDE; harmonic coordinate
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, Yizhao T.
Thesis Committee:
  • Hou, Yizhao T. (chair)
  • Beck, James L.
  • Bruno, Oscar P.
  • Owhadi, Houman
Defense Date:7 March 2014
Record Number:CaltechTHESIS:03312014-014047677
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:03312014-014047677
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8174
Collection:CaltechTHESIS
Deposited By: Maolin Ci
Deposited On:28 Apr 2014 21:49
Last Modified:28 Apr 2014 21:49

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