Please use this identifier to cite or link to this item:
|Title:||Karhunen-Loéve expansion of stochastic processes with a modified exponential covariance kernel|
|Keywords:||Computer aided simulation|
Monte Carlo method
|Source:||Spanos, P.D., Beer, M., Red-Horse, J. (2007-07). Karhunen-Loéve expansion of stochastic processes with a modified exponential covariance kernel. Journal of Engineering Mechanics 133 (7) : 773-779. ScholarBank@NUS Repository. https://doi.org/10.1061/(ASCE)0733-9399(2007)133:7(773)|
|Abstract:||The spectral representation of stationary stochastic processes via the Karhunen-Loéve (KL) expansion is examined from a numerical efficiency perspective. Attention is focused on processes which have commonly been characterized by covariance kernels decaying exponentially versus the position/time delay variable. By introducing a slight modification in the mathematical description of this covariance kernel, the nondifferentiability at its peak is eliminated, whereas most of its advantageous properties are retained. It is shown that compared to the common exponential model, the requisite number of terms for representing the process in context with the modified kernel is significantly smaller. The effect is demonstrated by means of a specific numerical example. This is done by first determining the eigenfunctions/ eigenvalues associated with the KL expansion for the modified kernel model, and by afterwards estimating the approximation errors corresponding to the two kernels considered for specific numerical values. Clearly, the enhanced computational efficiency of the KL expansion associated with the modified kernel can significantly expedite its incorporation in stochastic finite elements and other areas of stochastic mechanics. © 2007 ASCE.|
|Source Title:||Journal of Engineering Mechanics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 6, 2017
WEB OF SCIENCETM
checked on Nov 17, 2017
checked on Dec 10, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.