A Non-Intrusive Method to Inferring Linear Port-Hamiltonian Realizations using Time-Domain Data
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Abstract (arXiv)
Port-Hamiltonian systems have gained a lot of attention in recent years due to their inherent valuable properties in modeling and control. In this paper, we are interested in constructing linear port-Hamiltonian systems from time-domain input-output data. We discuss a non-intrusive methodology that is comprised of two main ingredients -- (a) inferring frequency response data from time-domain data and (b) constructing an underlying port-Hamiltonian realization using the inferred frequency response data. We illustrate the proposed methodology by means of two numerical examples and also compare it with two other system identification methods to infer the frequency response from the input-output data.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 May 2020.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 2.
5 papers.
- 2022Pseudo-Hamiltonian Neural Networks with State-Dependent External Forcescited by 17
- 2022Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networkscited by 17
- 2024Data-driven identification of latent port-Hamiltonian systemscited by 3
- 2024Data-driven identification of port-Hamiltonian DAE systems by Gaussian processescited by 2
- 2023Pseudo-Hamiltonian system identificationcited by 1
- 2024Data-driven identification of latent port-Hamiltonian systems15 Aug 2024
- 2024Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes26 Jun 2024
- 2023Pseudo-Hamiltonian system identification9 May 2023
- 2022Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks1 Dec 2022
- 2022Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces6 Jun 2022