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dc.contributor.authorSengul, Suleyman
dc.contributor.authorBekiryazici, Zafer
dc.contributor.authorMerdan, Mehmet
dc.date.accessioned2021-11-09T19:48:38Z
dc.date.available2021-11-09T19:48:38Z
dc.date.issued2021
dc.identifier.issn0354-9836
dc.identifier.issn2334-7163
dc.identifier.urihttps://doi.org/10.2298/TSCI200528014S
dc.identifier.urihttps://hdl.handle.net/20.500.12440/3696
dc.description.abstractIn this paper, Wong-Zakai approximation methods are presented for some stochastic differential equations in engineering sciences. Wong-Zakai approximate solutions of the equations are analyzed and the numerical results are compared with results from popular approximation schemes for stochastic differential equations such as Euler-Alartiyama and Milstein methods. Several differential equations from engineering problems containing stochastic noise are investigated as numerical examples. Results show that Wong-Zakai method is a reliable tool for studying stochastic differential equations and can he used as an alternative for the known approximation techniques for stochastic models.en_US
dc.language.isoengen_US
dc.publisherVinca Inst Nuclear Scien_US
dc.relation.ispartofThermal Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWong-Zakai approximationen_US
dc.subjectstochastic differential equationen_US
dc.subjectEuler-Maruyama methoden_US
dc.subjectMilstein methoden_US
dc.subjectsolar irradianceen_US
dc.subjectBrownian motionen_US
dc.titleWONG-ZAKAI METHOD FOR STOCHASTIC DIFFERENTIAL EQUATIONS IN ENGINEERINGen_US
dc.typearticleen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.description.wospublicationidWOS:000637582200014en_US
dc.description.scopuspublicationid2-s2.0-85101626407en_US
dc.departmentGümüşhane Üniversitesien_US
dc.authoridBekiryazici, Zafer / 0000-0001-5671-9995
dc.identifier.volume25en_US
dc.identifier.startpageS131en_US
dc.identifier.doi10.2298/TSCI200528014S
dc.identifier.endpageS142en_US
dc.authorwosidBekiryazici, Zafer / AAQ-7932-2020
dc.authorscopusid57193537709
dc.authorscopusid56191618000
dc.authorscopusid34980002400


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