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Machine learning inspired models for Hall effects in non-collinear magnets

; ; ; ; ; ;

In
Machine learning: science and technology 5(2), Seiten/Artikel-Nr.:025060

ImpressumBristol : IOP Publishing

Umfang[1]-24

ISSN2632-2153

Online
DOI: 10.1088/2632-2153/ad51ca

DOI: 10.18154/RWTH-2024-08225
URL: https://publications.rwth-aachen.de/record/992508/files/992508.pdf

Einrichtungen

  1. Lehr- und Forschungsgebiet Neural Computation (FZ Jülich) (124920)
  2. Fachgruppe Informatik (120000)
  3. Lehrstuhl für Theoretische Physik (FZ Jülich) (137510)
  4. Fachgruppe Physik (130000)


Thematische Einordnung (Klassifikation)
DDC: 621.3

OpenAccess:
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Dokumenttyp
Journal Article

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85195668803
WOS Core Collection: WOS:001248895600001

Interne Identnummern
RWTH-2024-08225
Datensatz-ID: 992508

Beteiligte Länder
Germany, USA

 GO

Medline ; Creative Commons Attribution CC BY 4.0 ; DOAJ ; OpenAccess ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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Document types > Articles > Journal Articles
Faculty of Mathematics, Computer Science and Natural Sciences (Fac.1) > Department of Computer Science
Faculty of Mathematics, Computer Science and Natural Sciences (Fac.1) > Department of Physics
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 Record created 2024-09-05, last modified 2025-02-07
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