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arXiv · 2501.06118

Nonlinear port-Hamiltonian system identification from input-state-output data (ISO-pHNN)

Karim Cherifi·Achraf El Messaoudi·Hannes Gernandt·Marco Roschkowski
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Abstract

In this paper, we introduce a framework called ISO-pHNN for identifying nonlinear port-Hamiltonian systems using input-state-output data. The framework utilizes neural networks' universal approximation capacity to effectively represent complex dynamics in a structured way. We explore different architectures based on MLPs, KANs, and using prior information. The identification technique is validated through examples featuring nonlinearities in either the skew-symmetric terms, the dissipative terms, or the Hamiltonian. We show that incorporating a port-Hamiltonian structure does not lower the accuracy and that using additional prior information improves long-term predictions.

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Karim Cherifi, Achraf El Messaoudi, Hannes Gernandt, Marco Roschkowski. 2026-08-20. Nonlinear port-Hamiltonian system identification from input-state-output data (ISO-pHNN). https://arxiv.org/abs/2501.06118

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