Record link: 
http://hdl.handle.net/20.500.12708/202603
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Title: 
Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium
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Citation: 
Carnazza, F., Carollo, F., Andergassen, S., Martius, G., Klopotek, M., & Lesanovsky, I. (2024). Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium. MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 5(4), Article 045002. https://doi.org/10.1088/2632-2153/ad7ad7
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Publisher DOI: 
10.1088/2632-2153/ad7ad7
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Carnazza, Francesco 
Carollo, Federico 
Andergassen, Sabine 
Martius, Georg 
Klopotek, Miriam 
Lesanovsky, Igor 
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Organisational Unit: 
E194-06 - Forschungsbereich Machine Learning 
E138-02 - Forschungsbereich Correlations: Theory and Experiments 
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML) 
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Journal: 
MACHINE LEARNING-SCIENCE AND TECHNOLOGY 
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ISSN: 
2632-2153
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Date (published): 
7-Oct-2024
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Number of Pages: 
16
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Publisher: 
IOP PUBLISHING LTD
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Peer reviewed: 
Yes
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Keywords: 
Machine learning; stochastic differential equations
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Research Areas: 
Quantum Many-body Systems Physics: 50%
Information Systems Engineering: 50%
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Science Branch: 
1020 - Informatik: 50%
1030 - Physik, Astronomie: 50%
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