Learning Mechanical Systems by Hamiltonian Neural Networks
Abstract: The great success of machine learning in image processing and related fields also motivated its application to dynamical system identification. In particular, neural networks are trained to learn equations of motion and thus provide an alternative to first‐principle modeling. While these black‐box algorithms are quite flexible regarding the system structure, they often have difficulties in learning basic physics laws which are intrinsic system properties. Recently, Hamiltonian neural networks (HNN) were introduced to explicitly learn the total energy of a system in order to overcome the lack of physical rules. However, Hamiltonian systems often also contain other structures such as symmetries in terms of further invariances. In this contribution, we extend HNN such they respect system invariances in addition to the Hamiltonian. The proposed extension leads to a trade off between energy conservation and conservation of invariance properties, which we investigate exemplarily.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Learning Mechanical Systems by Hamiltonian Neural Networks ; volume:21 ; number:1 ; year:2021 ; extent:2
Proceedings in applied mathematics and mechanics ; 21, Heft 1 (2021) (gesamt 2)
- Urheber
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Dierkes, Eva
Flaßkamp, Kathrin
- DOI
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10.1002/pamm.202100116
- URN
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urn:nbn:de:101:1-2021121514094428204592
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:21 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Dierkes, Eva
- Flaßkamp, Kathrin
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