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dc.contributor.authorMaulik, R
dc.contributor.authorGarland, NA
dc.contributor.authorBurby, JW
dc.contributor.authorTang, XZ
dc.contributor.authorBalaprakash, P
dc.date.accessioned2021-08-06T00:48:18Z
dc.date.available2021-08-06T00:48:18Z
dc.date.issued2020
dc.identifier.issn1070-664X
dc.identifier.doi10.1063/5.0006457
dc.identifier.urihttp://hdl.handle.net/10072/406636
dc.description.abstractThe closure problem in fluid modeling is a well-known challenge to modelers aiming to accurately describe their systems of interest. Over many years, analytic formulations in a wide range of regimes have been presented but a practical, generalized fluid closure for magnetized plasmas remains an elusive goal. In this study, as a first step toward constructing a novel data-based approach to this problem, we apply ever-maturing machine learning methods to assess the capability of neural network architectures to reproduce crucial physics inherent in known magnetized plasma closures. We find encouraging results, indicating the applicability of neural networks to closure physics, but also arrive at recommendations on how one should choose appropriate network architectures for the given locality properties dictated by the underlying physics of the plasma.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAIP Publishing
dc.publisher.urihttps://aip.scitation.org/journal/php
dc.relation.ispartofissue7
dc.relation.ispartofjournalPhysics of Plasmas
dc.relation.ispartofvolume27
dc.subject.fieldofresearchNeural networks
dc.subject.fieldofresearchPlasma physics; fusion plasmas; electrical discharges
dc.subject.fieldofresearchNumerical and computational mathematics
dc.subject.fieldofresearchcode461104
dc.subject.fieldofresearchcode510602
dc.subject.fieldofresearchcode4903
dc.subject.keywordsphysics.comp-ph
dc.subject.keywordsphysics.plasm-ph
dc.titleNeural network representability of fully ionized plasma fluid model closures
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationMaulik, R; Garland, NA; Burby, JW; Tang, XZ; Balaprakash, P, Neural network representability of fully ionized plasma fluid model closures, Physics of Plasmas, 2020, 27 (7), Arcticle 072106. DOI: https://doi.org/10.1063/5.0006457
dc.date.updated2021-08-04T22:47:58Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Maulik, R; Garland, NA; Burby, JW; Tang, XZ; Balaprakash, P, Neural network representability of fully ionized plasma fluid model closures, Physics of Plasmas, 2020, 27 (7), Arcticle 072106, and may be found at https://doi.org/10.1063/5.0006457
gro.hasfulltextFull Text
gro.griffith.authorGarland, Nathan


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