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  Data-driven structure-preserving model reduction for stochastic Hamiltonian systems

Tyranowski, T. (2024). Data-driven structure-preserving model reduction for stochastic Hamiltonian systems. Journal of Computational Dynamics, 11(2), 220-255. doi:10.3934/jcd.2024010.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000E-A8F2-3 Version Permalink: https://hdl.handle.net/21.11116/0000-000F-1B61-6
Genre: Journal Article

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 Creators:
Tyranowski, T.1, Author                 
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1Numerical Methods in Plasma Physics (NMPP), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856344              

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Language(s): eng - English
 Dates: 20222024
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.3934/jcd.2024010
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Title: Journal of Computational Dynamics
  Abbreviation : JCD
Source Genre: Journal
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Publ. Info: Springfield, MO : American Institute of Mathematical Sciences (AIMS)
Pages: - Volume / Issue: 11 (2) Sequence Number: - Start / End Page: 220 - 255 Identifier: ISSN: 2158-2505
CoNE: https://pure.mpg.de/cone/journals/resource/2158-2505