Machine learning techniques in magnetic levitation problems

dc.contributor.authorArrayás, Manuel
dc.contributor.authorTrueba, José L.
dc.contributor.authorUriarte, Carlos
dc.date.accessioned2023-09-22T10:45:24Z
dc.date.available2023-09-22T10:45:24Z
dc.date.issued2022
dc.descriptionThis work was funded by Universidad Rey Juan Carlos, Spain , Programa Propio: Analysis, modelling and simulations of singular structures in continuum models, M2604.es
dc.description.abstractWe present a method for calculating the stability region of a perfect diamagnet levitated in a magnetic field created by a circular current loop making use of the machine learning techniques. As an application we compute stability regions, points of stable equilibrium and stable oscillatory motions in two chip-based superconducting trap architectures used to levitate superconducting particles. Our procedure is an alternative to a full numerical scheme based on finite element methods which are expensive to implement for optimizing experimental parameters.es
dc.identifier.citationManuel Arrayás, José L. Trueba, Carlos Uriarte, Machine learning techniques in magnetic levitation problems, Chaos, Solitons & Fractals, Volume 167, 2023, 113043, ISSN 0960-0779, https://doi.org/10.1016/j.chaos.2022.113043es
dc.identifier.doi10.1016/j.chaos.2022.113043es
dc.identifier.issn0960-0779
dc.identifier.urihttps://hdl.handle.net/10115/24485
dc.language.isoenges
dc.publisherElsevieres
dc.rightsAtribución 4.0 Internacional*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMagnetic levitationes
dc.subjectMachine learninges
dc.subjectStability regionses
dc.titleMachine learning techniques in magnetic levitation problemses
dc.typeinfo:eu-repo/semantics/articlees

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