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Learning reduced-order models for dynamic CO2 methanation using operator inference

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Peterson,  Luisa
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Bremer,  Jens
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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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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Gosea,  Ion Victor
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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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;

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Sundmacher,  Kai       
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Citation

Peterson, L., Bremer, J., Goyal, P. K., Gosea, I. V., Benner, P., & Sundmacher, K. (2024). Learning reduced-order models for dynamic CO2 methanation using operator inference. Talk presented at ESCAPE-34. Florence, Italy. 2024-06-02 - 2024-06-06.


Cite as: https://hdl.handle.net/21.11116/0000-000F-FE98-8
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