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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. (in preparation). Operator Inference and Physics-Informed Learning of Low-Dimensional Models for Incompressible Flows.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0007-378A-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0007-378B-F
Genre: Paper

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2010.06701.pdf (Preprint), 2MB
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File downloaded from arXiv at 2020-10-15 10:01
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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              
2OvGU, ou_persistent22              

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 Dates: 2020-10-13
 Publication Status: Not specified
 Pages: -
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 Rev. Type: -
 Identifiers: arXiv: 2010.06701
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