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,
Roger Ghanem, (M.ASCE), Assoc. Prof.; Dept. of Civ. Engrg., Johns Hopkins Univ., Baltimore, MD 21218., ghanem@jhu.edu,

Serial Information: Issue 2, Pg. 190-201

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.

Subject Headings: Gaussian process | Polynomials | Decomposition | Stationary processes | Finite element method | Correlation |

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