1.
Cho, Heyrim.
High-Dimensional Response-Excitation PDF Methods for
Uncertainty Quantification and Stochastic Modeling.
Degree: PhD, Applied Mathematics, 2015, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:419509/
The probability density approach based on the
response-excitation theory is developed for stochastic simulations
of non-Markovian systems. This approach provides the complete
probabilistic configuration of the solution that enables a
comprehensive study of stochastic systems. By using functional
integral methods we determine a computable evolution equation for
the joint response-excitation probability density function (REPDF)
of stochastic dynamical systems and stochastic partial differential
equations driven by colored noise. We establish its connection to
the classical response approach and its agreement to the
Dostupov-Pugachev equations (Dostupov, 1957) and the
Malakhov-Saichev equations (Gurbatov et al, 1991). An efficient
algorithm has been proposed by using adaptive discontinuous
Galerkin method and probabilistic collocation method combined with
sparse grid. For high-dimensional REPDF systems, we develop the
algorithms concerning high-dimensional numerical approximations,
namely, separated series expansion and the ANOVA approximation.
These methods reduce the computational cost in high-dimensions to
several low-dimensional operations. Alternatively, reduced order
PDF equations are obtained by using the Mori-Zwanzig framework and
conditional moment closures, which establish a preliminary work of
goal-oriented PDF equations. Finally, we demonstrate the
effectiveness of the proposed numerical methods to various
stochastic systems including the tumor cell growth model, chaotic
nonlinear oscillators, advection reaction equation, and Burgers
equation. The second part of the thesis focuses on simulations of
multi-scale stochastic systems. The Karhunen-Loeve expansion is
extended to characterize multiple correlated random processes and
local decomposed random fields. We then propose interface
conditions based on conditional moments and PDE-constrained
optimization that preserve the global statistics while propagating
uncertainty. Finally, the decomposition algorithm is recast to
couple distinct PDF models including the REPDF
system.
Advisors/Committee Members: Karniadakis, George (Director), Rozovsky, Boris (Reader), Venturi, Daniele (Reader), Sapsis, Themistoklis (Reader).
Subjects/Keywords: Uncertainty quantification
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APA (6th Edition):
Cho, H. (2015). High-Dimensional Response-Excitation PDF Methods for
Uncertainty Quantification and Stochastic Modeling. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:419509/
Chicago Manual of Style (16th Edition):
Cho, Heyrim. “High-Dimensional Response-Excitation PDF Methods for
Uncertainty Quantification and Stochastic Modeling.” 2015. Doctoral Dissertation, Brown University. Accessed January 16, 2021.
https://repository.library.brown.edu/studio/item/bdr:419509/.
MLA Handbook (7th Edition):
Cho, Heyrim. “High-Dimensional Response-Excitation PDF Methods for
Uncertainty Quantification and Stochastic Modeling.” 2015. Web. 16 Jan 2021.
Vancouver:
Cho H. High-Dimensional Response-Excitation PDF Methods for
Uncertainty Quantification and Stochastic Modeling. [Internet] [Doctoral dissertation]. Brown University; 2015. [cited 2021 Jan 16].
Available from: https://repository.library.brown.edu/studio/item/bdr:419509/.
Council of Science Editors:
Cho H. High-Dimensional Response-Excitation PDF Methods for
Uncertainty Quantification and Stochastic Modeling. [Doctoral Dissertation]. Brown University; 2015. Available from: https://repository.library.brown.edu/studio/item/bdr:419509/