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  Operator Inference and Physics-Informed Learning of Low-Dimensional Models for Incompressible Flows

Benner, P., Goyal, P. K., Heiland, J., & Pontes Duff, I. (2022). Operator Inference and Physics-Informed Learning of Low-Dimensional Models for Incompressible Flows. Electronic Transactions on Numerical Analysis: Special Issue SciML, 56, 28-51. doi:10.1553/etna_vol56s28.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-378A-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0009-C380-9
Genre: Journal Article

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 Creators:
Benner, Peter1, 2, Author           
Goyal, Pawan Kumar1, Author           
Heiland, Jan1, 2, Author           
Pontes Duff, Igor1, Author           
Affiliations:
1Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738141              
2Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              

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 Dates: 2020-10-132022
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: arXiv: 2010.06701
DOI: 10.1553/etna_vol56s28
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Title: Electronic Transactions on Numerical Analysis : Special Issue SciML
  Other : ETNA
Source Genre: Journal
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Publ. Info: Kent, OH : Kent State University
Pages: - Volume / Issue: 56 Sequence Number: - Start / End Page: 28 - 51 Identifier: ISSN: 1068-9613
CoNE: https://pure.mpg.de/cone/journals/resource/954926402638