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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 | Citation: | 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 |
Appears in Collections: | Staff Publications |
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