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

arXiv: https://arxiv.org/abs/2501.06118 · HTML: https://mlsanity.com/paper/2501.06118/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2025.

## Built on by (most cited first, 6 of 6)

- [State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification](https://mlsanity.com/paper/2506.16392/) (2025)
- [Controlled oscillation modeling using port-Hamiltonian neural networks](https://mlsanity.com/paper/2602.15704/) (2026)
- [PH-KAN: Port-Hamiltonian Kolmogorov-Arnold Network](https://mlsanity.com/paper/2606.14708/) (2026)
- [Structure- and Stability-Preserving Learning of Port-Hamiltonian Systems](https://mlsanity.com/paper/2604.13297/) (2026)
- [Identifying the nonlinear string dynamics with port-Hamiltonian neural networks](https://mlsanity.com/paper/2605.12785/) (2026)
- [Port-Hamiltonian Neural Networks for Systems with Multiple Asymptotically Stable Equilibria](https://mlsanity.com/paper/2610.01356/) (2026)

## Abstract (arXiv)

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.