Title Information
Title
Towards High-order Methods for Stochastic Differential Equations with White Noise: A Spectral Approach
Name: Personal
Name Part
zhang, zhongqiang
Role
Role Term: Text
creator
Origin Information
Copyright Date
2014
Physical Description
Extent
xvi, 285 p.
digitalOrigin
born digital
Note
Thesis (Ph.D. -- Brown University (2014)
Name: Personal
Name Part
Karniadakis, George
Role
Role Term: Text
Director
Name: Personal
Name Part
Rozovskii, Boris
Role
Role Term: Text
Director
Name: Personal
Name Part
Tretyakov, Michael
Role
Role Term: Text
Reader
Name: Personal
Name Part
Sarkis, Marcus
Role
Role Term: Text
Reader
Name: Corporate
Name Part
Brown University. Applied Mathematics
Role
Role Term: Text
sponsor
Genre (aat)
theses
Abstract
We develop a recursive multistage Wiener chaos expansion method (WCE) and a recursive multi-stage stochastic collocation method (SCM) for numerical integration of linear stochastic advection-diffusion-reaction equations with multiplicative white noise. We show that both methods are comparably efficient in computing the first two moments of solutions for long time intervals, compared to a direct application of WCE and SCM while both methods are more efficient than the standard Monte Carlo method if high accuracy is required. Both methods belong to Wong-Zakai approximation, where Brownian motion is truncated using its spectral expansion before any discretization in time and space. For computational convenience, WCE is associated with the Ito formulation of underlying equations and SCM is associated with the Stratonovich formulation. We apply SCM using Smolyak’s sparse grid construction to obtain the shock location of the one-dimensional piston problem, which is modeled by stochastic Euler equations with multiplicative white noise. We show numerically that SCM is efficient for short time simulations and for small magnitudes of noises and quasi-Monte Carlo methods are efficient for moderate large-time simulations. We also illustrate the efficiency of SCM through error estimates for a linear model problem. We further investigate the effect of a spectral approximation of Brownian motion, rather than a piecewise linear approximation, for both spatial and temporal noise. For spatial noise, we consider semilinear elliptic equations with additive noise and show that when the solution is smooth enough, the spectral approximation is superior to the piecewise linear approximation while both approximations are comparable when the solution is not smooth. For temporal noise, we use this spectral approach to design numerical schemes for stochastic delay differential equations under the Stratonovich formulation. We show that the spectral approach admits higher-order accuracy only for higher-order schemes. Besides equations with coefficients of linear growth, we also consider stochastic ordinary differential equations with coefficients of polynomial growth. We formulate a basic relationship between local truncation error and global error of numerical methods for these equations, and apply this relationship for our explicit balanced scheme to obtain the convergence order.
Subject
Topic
Wiener chaos expansion
Subject
Topic
stochastic collocation method
Subject
Topic
stochastic differential equation
Subject
Topic
Brownian motion
Subject
Topic
spectral approximation
Subject
Topic
time integration
Subject
Topic
strong and weak convergence
Subject
Topic
numerical schemes
Subject
Topic
Wong-Zakai approximation
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1133506")
Topic
Stochastic differential equations
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/839765")
Topic
Brownian motion processes
Record Information
Record Content Source (marcorg)
RPB
Record Creation Date (encoding="iso8601")
20141006
Language
Language Term: Code (ISO639-2B)
eng
Language Term: Text
English
Identifier: DOI
10.7301/Z06Q1VKH
Access Condition: rights statement (href="http://rightsstatements.org/vocab/InC/1.0/")
In Copyright
Access Condition: restriction on access
Collection is open for research.
Type of Resource (primo)
dissertations