An enhanced hybrid method for the simulation of highly skewed non-Gaussian stochastic fields

Lagaros, ND; Stefanou, G; Papadrakakis, M

dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Stefanou, G	en
dc.contributor.author	Papadrakakis, M	en
dc.date.accessioned	2014-03-01T01:21:48Z
dc.date.available	2014-03-01T01:21:48Z
dc.date.issued	2005	en
dc.identifier.issn	0045-7825	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16385
dc.subject	Non-Gaussian field	en
dc.subject	Soft computing	en
dc.subject	Translation field	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Mechanics	en
dc.subject.other	Approximation theory	en
dc.subject.other	Computational methods	en
dc.subject.other	Functions	en
dc.subject.other	Spectrum analysis	en
dc.subject.other	Enhanced hybrid methods (EHM)	en
dc.subject.other	Non-gaussian stochastic fields	en
dc.subject.other	Resilient back-propagation (Rprop)	en
dc.subject.other	Spectral density functions	en
dc.subject.other	Random processes	en
dc.subject.other	non-Gaussian response	en
dc.title	An enhanced hybrid method for the simulation of highly skewed non-Gaussian stochastic fields	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cma.2004.12.009	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cma.2004.12.009	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	In this paper, an enhanced hybrid method (EHM) is presented for the simulation of homogeneous non-Gaussian stochastic fields with prescribed target marginal distribution and spectral density function. The presented methodology constitutes an efficient blending of the Deodatis-Micaletti method with a neural network based function approximation. Precisely, the function fitting ability of neural networks based on the resilient back-propagation (Rprop) learning algorithm is employed to approximate the unknown underlying Gaussian spectrum. The resulting algorithm can be successfully applied for simulating narrow-banded fields with very large skewness at a fraction of the computing time required by the existing methods. Its computational efficiency is demonstrated in three numerical examples involving fields that follow the beta and lognormal distributions. (c) 2005 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Computer Methods in Applied Mechanics and Engineering	en
dc.identifier.doi	10.1016/j.cma.2004.12.009	en
dc.identifier.isi	ISI:000231533400010	en
dc.identifier.volume	194	en
dc.identifier.issue	45-47	en
dc.identifier.spage	4824	en
dc.identifier.epage	4844	en