Magnetic field mapping of inaccessible regions using physics-informed neural networks

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dc.contributor.authorCoskun, Umit H.en
dc.contributor.authorSel, Bilgehanen
dc.contributor.authorPlaster, Braden
dc.date.accessioned2022-10-25T14:02:58Zen
dc.date.available2022-10-25T14:02:58Zen
dc.date.issued2022-07-27en
dc.description.abstractA difficult problem concerns the determination of magnetic field components within an experimentally inaccessible region when direct field measurements are not feasible. In this paper, we propose a new method of accessing magnetic field components using non-disruptive magnetic field measurements on a surface enclosing the experimental region. Magnetic field components in the experimental region are predicted by solving a set of partial differential equations (Ampere's law and Gauss' law for magnetism) numerically with the aid of physics-informed neural networks (PINNs). Prediction errors due to noisy magnetic field measurements and small number of magnetic field measurements are regularized by the physics information term in the loss function. We benchmark our model by comparing it with an older method. The new method we present will be of broad interest to experiments requiring precise determination of magnetic field components, such as searches for the neutron electric dipole moment.en
dc.description.notesThis material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under Award Number DE-SC0014622.en
dc.description.sponsorshipU.S. Department of Energy, Office of Science, Office of Nuclear Physics [DE-SC0014622]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41598-022-15777-4en
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.other12858en
dc.identifier.pmid35896568en
dc.identifier.urihttp://hdl.handle.net/10919/112273en
dc.identifier.volume12en
dc.language.isoenen
dc.publisherNature Portfolioen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleMagnetic field mapping of inaccessible regions using physics-informed neural networksen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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