Polynomial Chaos Decomposition for the Simulation of Non-Gaussian Nonstationary Stochastic Processes
by Shigehiro Sakamoto, (Earthquake and Wind Engrg. Section, Building Res. Dept., Taisei Res. Inst., Taisei Corporation, Nase-cho 334-1, Totsukaku Yokohama 245-0051, Japan) and Roger Ghanem, M.ASCE, (Assoc. Prof., Dept. of Civ. Engrg., Johns Hopkins Univ., Baltimore, MD 21218. E-mail: ghanem@jhu.edu)
Journal of Engineering Mechanics, Vol. 128, No. 2, February 2002, pp. 190-201, (doi: http://dx.doi.org/10.1061/(ASCE)0733-9399(2002)128:2(190))
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| Document type: |
Journal Paper |
| Abstract: |
A method is developed for representing and synthesizing random processes that have been specified by their two-point correlation function and their nonstationary marginal probability density functions. The target process is represented as a polynomial transformation of an appropriate Gaussian process. The target correlation structure is decomposed according to the Karhunen-Loève expansion of the underlying Gaussian process. A sequence of polynomial transformations in this process is then used to match the one-point marginal probability density functions. The method results in a representation of a stochastic process that is particularly well suited for implementation with the spectral stochastic finite element method as well as for general purpose simulation of realizations of these processes. |
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