Record link: 
http://hdl.handle.net/20.500.12708/227799
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Title: 
Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
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Citation: 
Egenlauf, P., Březinová, I., Andergassen, S., & Klopotek, M. (2026). Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations. MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 7(2), 025062. https://doi.org/10.1088/2632-2153/ae57f8
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Publisher DOI: 
10.1088/2632-2153/ae57f8
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Egenlauf, Patrick 
Březinová, Iva 
Andergassen, Sabine 
Klopotek, Miriam 
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Organisational Unit: 
E194-06 - Forschungsbereich Machine Learning 
E138-02 - Forschungsbereich Correlations: Theory and Experiments 
E136 - Institut für Theoretische Physik 
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML) 
E056-28 - Fachbereich Computational Sustainability 
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Journal: 
MACHINE LEARNING-SCIENCE AND TECHNOLOGY 
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ISSN: 
2632-2153
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Date (published): 
2026
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Publisher: 
IOP PUBLISHING LTD
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Peer reviewed: 
Yes
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Keywords: 
machine learning; quantum dynamics; reduced density matrix
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Research Areas: 
Quantum Many-body Systems Physics: 50%
Information Systems Engineering: 50%
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Science Branch: 
1020 - Informatik: 90%
5020 - Wirtschaftswissenschaften: 10%
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