Machine learning techniques in magnetic levitation problems
dc.contributor.author | Arrayás, Manuel | |
dc.contributor.author | Trueba, José L. | |
dc.contributor.author | Uriarte, Carlos | |
dc.date.accessioned | 2023-09-22T10:45:24Z | |
dc.date.available | 2023-09-22T10:45:24Z | |
dc.date.issued | 2022 | |
dc.description | This 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.abstract | We 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.citation | Manuel 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.2022.113043 | es |
dc.identifier.doi | 10.1016/j.chaos.2022.113043 | es |
dc.identifier.issn | 0960-0779 | |
dc.identifier.uri | https://hdl.handle.net/10115/24485 | |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Magnetic levitation | es |
dc.subject | Machine learning | es |
dc.subject | Stability regions | es |
dc.title | Machine learning techniques in magnetic levitation problems | es |
dc.type | info:eu-repo/semantics/article | es |
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