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

Authors

K. Cherifi, A. El Messaoudi, H. Gernandt, M. Roschkowski

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.

Keywords

dissipative systems, dynamical systems, long-term prediction, nonlinear system identification, physics-informed machine learning, port-hamiltonian systems, structure-preserving learning

Citation

• Journal: Physica D: Nonlinear Phenomena
• Year: 2026
• Volume: 497
• Issue:
• Pages: 135368
• Publisher: Elsevier BV
• DOI: 10.1016/j.physd.2026.135368

BibTeX

@article{Cherifi_2026,
  title={{Nonlinear port-Hamiltonian system identification from input-state-output data (ISO-pHNN)}},
  volume={497},
  ISSN={0167-2789},
  DOI={10.1016/j.physd.2026.135368},
  journal={Physica D: Nonlinear Phenomena},
  publisher={Elsevier BV},
  author={Cherifi, K. and El Messaoudi, A. and Gernandt, H. and Roschkowski, M.},
  year={2026},
  pages={135368}
}

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