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MACHINE LEARNING AND NEURAL NETWORKS BASED APPROACH FOR DEFLECTION PREDICTION OF EULER-BERNOULLI BEAM EQUATIONS

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dc.contributor.authorRasulov, Zaur
dc.contributor.authorYesil, Ulku Babuscu
dc.date.accessioned2026-06-27T14:47:51Z
dc.date.issued2023
dc.description.abstractBeam-like structures are widespread but essential systems that have been extensively studied for centuries. Several proposed solutions have been effective, but not all are suitable because they are timeconsuming and lack accuracy. This paper offers a new methodology for finding solutions to beam problems based on machine learning and neural networks with different optimization algorithms. Different regression models are compared on numerically stimulated Euler-Bernoulli beam modelling.en
dc.identifier.endpage158
dc.identifier.issn1221-5872
dc.identifier.issue1
dc.identifier.startpage149
dc.identifier.urihttps://hdl.handle.net/20.500.14981/64894
dc.identifier.volume66
dc.identifier.wos000995805400017
dc.language.isoeng
dc.publisherTECHNICAL UNIV CLUJ-NAPOCA, FAC MACHINE BUILDING DEPT SYSTEMS ENG
dc.relation.ispartofACTA TECHNICA NAPOCENSIS SERIES-APPLIED MATHEMATICS MECHANICS AND ENGINEERING
dc.subjectMachine Learning
dc.subjectNeural Network
dc.subjectFEM
dc.subjectEuler-Bernoulli Beams
dc.subjectdeflection
dc.subjectMechanics
dc.titleMACHINE LEARNING AND NEURAL NETWORKS BASED APPROACH FOR DEFLECTION PREDICTION OF EULER-BERNOULLI BEAM EQUATIONS
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

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