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<titleInfo><title>Hamiltonian Neural Networks with Automatic Symmetry Detection</title></titleInfo>


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<name type="personal">
  <namePart type="given">Eva</namePart>
  <namePart type="family">Dierkes</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Christian</namePart>
  <namePart type="family">Offen</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">85279</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0002-5940-8057</description></name>
<name type="personal">
  <namePart type="given">Sina</namePart>
  <namePart type="family">Ober-Blöbaum</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">16494</identifier></name>
<name type="personal">
  <namePart type="given">Kathrin</namePart>
  <namePart type="family">Flaßkamp</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>







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<abstract lang="eng">Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when
learning the dynamical equations of Hamiltonian systems. Hereby, the symplectic system structure is preserved despite
the data-driven modeling approach. However, preserving symmetries requires additional attention. In this research, we
enhance the HNN with a Lie algebra framework to detect and embed symmetries in the neural network. This approach
allows to simultaneously learn the symmetry group action and the total energy of the system. As illustrating examples,
a pendulum on a cart and a two-body problem from astrodynamics are considered.</abstract>

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<originInfo><publisher>AIP Publishing</publisher><dateIssued encoding="w3cdtf">2023</dateIssued>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>Chaos</title></titleInfo>
  <identifier type="issn">1054-1500</identifier>
  <identifier type="arXiv">2301.07928</identifier><identifier type="doi">10.1063/5.0142969</identifier>
<part><detail type="volume"><number>33</number></detail><detail type="issue"><number>6</number></detail>
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  <location>
  
     <url>https://github.com/eva-dierkes/HNN_withSymmetries</url>
  
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<ama>Dierkes E, Offen C, Ober-Blöbaum S, Flaßkamp K. Hamiltonian Neural Networks with Automatic Symmetry Detection. &lt;i&gt;Chaos&lt;/i&gt;. 2023;33(6). doi:&lt;a href=&quot;https://doi.org/10.1063/5.0142969&quot;&gt;10.1063/5.0142969&lt;/a&gt;</ama>
<ieee>E. Dierkes, C. Offen, S. Ober-Blöbaum, and K. Flaßkamp, “Hamiltonian Neural Networks with Automatic Symmetry Detection,” &lt;i&gt;Chaos&lt;/i&gt;, vol. 33, no. 6, Art. no. 063115, 2023, doi: &lt;a href=&quot;https://doi.org/10.1063/5.0142969&quot;&gt;10.1063/5.0142969&lt;/a&gt;.</ieee>
<chicago>Dierkes, Eva, Christian Offen, Sina Ober-Blöbaum, and Kathrin Flaßkamp. “Hamiltonian Neural Networks with Automatic Symmetry Detection.” &lt;i&gt;Chaos&lt;/i&gt; 33, no. 6 (2023). &lt;a href=&quot;https://doi.org/10.1063/5.0142969&quot;&gt;https://doi.org/10.1063/5.0142969&lt;/a&gt;.</chicago>
<bibtex>@article{Dierkes_Offen_Ober-Blöbaum_Flaßkamp_2023, title={Hamiltonian Neural Networks with Automatic Symmetry Detection}, volume={33}, DOI={&lt;a href=&quot;https://doi.org/10.1063/5.0142969&quot;&gt;10.1063/5.0142969&lt;/a&gt;}, number={6063115}, journal={Chaos}, publisher={AIP Publishing}, author={Dierkes, Eva and Offen, Christian and Ober-Blöbaum, Sina and Flaßkamp, Kathrin}, year={2023} }</bibtex>
<short>E. Dierkes, C. Offen, S. Ober-Blöbaum, K. Flaßkamp, Chaos 33 (2023).</short>
<mla>Dierkes, Eva, et al. “Hamiltonian Neural Networks with Automatic Symmetry Detection.” &lt;i&gt;Chaos&lt;/i&gt;, vol. 33, no. 6, 063115, AIP Publishing, 2023, doi:&lt;a href=&quot;https://doi.org/10.1063/5.0142969&quot;&gt;10.1063/5.0142969&lt;/a&gt;.</mla>
<apa>Dierkes, E., Offen, C., Ober-Blöbaum, S., &amp;#38; Flaßkamp, K. (2023). Hamiltonian Neural Networks with Automatic Symmetry Detection. &lt;i&gt;Chaos&lt;/i&gt;, &lt;i&gt;33&lt;/i&gt;(6), Article 063115. &lt;a href=&quot;https://doi.org/10.1063/5.0142969&quot;&gt;https://doi.org/10.1063/5.0142969&lt;/a&gt;</apa>
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