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Active learning with neural ordinary differential equations for data-efficient modeling of tuned mass dampers

; ;

In
Engineering applications of artificial intelligence 173, Seiten/Artikel-Nr.:114487

ImpressumAmsterdam [u.a.] : Elsevier Science

Umfang[1]-20

ISSN0952-1976

Online
DOI: 10.1016/j.engappai.2026.114487

DOI: 10.18154/RWTH-2026-03225
URL: https://publications.rwth-aachen.de/record/1031978/files/1031978.pdf

Einrichtungen

  1. Lehrstuhl und Institut für Regelungstechnik (416610)
  2. Lehrstuhl und Institut für Baustatik und Baudynamik (311810)


Thematische Einordnung (Klassifikation)
DDC: 004

OpenAccess:
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Dokumenttyp
Journal Article

Format
online, print

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105032534659
WOS Core Collection: WOS:001727815300001

Interne Identnummern
RWTH-2026-03225
Datensatz-ID: 1031978

Beteiligte Länder
Germany

Lizenzstatus der Zeitschrift

 GO

Medline ; Creative Commons Attribution CC BY 4.0 ; OpenAccess ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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Faculty of Mechanical Engineering (Fac.4)
Faculty of Civil Engineering (Fac.3)
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416610
311810
 Record created 2026-03-16, last modified 2026-05-04
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