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Simulation of multi-dimensional Gaussian stochastic fields by spectral representation

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dc.contributor.author Shinozuka, M en
dc.contributor.author Deodatis, G en
dc.date.accessioned 2014-03-01T11:45:39Z
dc.date.available 2014-03-01T11:45:39Z
dc.date.issued 1996 en
dc.identifier.issn 00036900 en
dc.identifier.uri http://hdl.handle.net/123456789/37523
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-0029701179&partnerID=40&md5=d7b2e01565651dd066c6b07fbe2bcaf7 en
dc.title Simulation of multi-dimensional Gaussian stochastic fields by spectral representation en
heal.type other en
heal.publicationDate 1996 en
heal.abstract The subject of this paper is the simulation of multi-dimensional, homogeneous, Gaussian stochastic fields using the spectral representation method. Following this methodology, sample functions of the stochastic field can be generated using a cosine series formula. These sample functions accurately reflect the prescribed probabilistic characteristics of the stochastic field when the number of terms in the cosine series is large. The ensemble-averaged power spectral density or autocorrelation function approaches the corresponding target function as the sample size increases. In addition, the generated sample functions possess ergodic characteristics in the sense that the spatially-averaged mean value, autocorrelation function and power spectral density function are identical with the corresponding targets, when the averaging takes place over the multi-dimensional domain associated with the fundamental period of the cosine series. Another property of the simulated stochastic field is that it is asymptotically Gaussian as the number of terms in the cosine series approaches infinity. The most important feature of the method is that the cosine series formula can be numerically computed very efficiently using the Fast Fourier Transform technique. The main area of application of this method is the Monte Carlo solution of stochastic problems in structural engineering, engineering mechanics and physics. Specifically, the method has been applied to problems involving random loading (random vibration theory) and random material and geometric properties (response variability due to system stochasticity). en
heal.journalName Applied Mechanics Reviews en
dc.identifier.volume 49 en
dc.identifier.issue 1 en
dc.identifier.spage 29 en
dc.identifier.epage 53 en


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