Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.probengmech.2005.05.007
Title: Simulation of strongly non-Gaussian processes using Karhunen-Loeve expansion
Authors: Phoon, K.K. 
Huang, H.W.
Quek, S.T. 
Keywords: Karhunen-Loeve expansion
Latin hypercube orthogonalization
Non-Gassian marginal distribution
Non-stationary covariance
Simulation
Stationary covariance
Issue Date: Apr-2005
Source: Phoon, K.K., Huang, H.W., Quek, S.T. (2005-04). Simulation of strongly non-Gaussian processes using Karhunen-Loeve expansion. Probabilistic Engineering Mechanics 20 (2) : 188-198. ScholarBank@NUS Repository. https://doi.org/10.1016/j.probengmech.2005.05.007
Abstract: The non-Gaussian Karhunen-Loeve (K-L) expansion is very attractive because it can be extended readily to non-stationary and multi-dimensional fields in a unified way. However, for strongly non-Gaussian processes, the original procedure is unable to match the distribution tails well. This paper proposes an effective solution to this tail mismatch problem using a modified orthogonalization technique that reduces the degree of shuffling within columns containing empirical realizations of the K-L random variables. Numerical examples demonstrate that the present algorithm is capable of matching highly non-Gaussian marginal distributions and stationary/non-stationary covariance functions simultaneously to a very accurate degree. The ability to converge correctly to an abrupt lower bound in the target marginal distributions studied is noteworthy. © 2005 Elsevier Ltd. All rights reserved.
Source Title: Probabilistic Engineering Mechanics
URI: http://scholarbank.nus.edu.sg/handle/10635/66175
ISSN: 02668920
DOI: 10.1016/j.probengmech.2005.05.007
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