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

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Benner,  Peter
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Goyal,  Pawan Kumar
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Heiland,  Jan
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Pontes Duff,  Igor
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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2010.06701.pdf
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Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-0007-378A-0
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