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Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics

MPS-Authors
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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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Yildiz,  Süleyman
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;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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2308.13835.pdf
(Preprint), 5MB

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Citation

Goyal, P. K., Yildiz, S., & Benner, P. (in preparation). Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics.


Cite as: https://hdl.handle.net/21.11116/0000-000D-B6D8-2
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