# Port-Hamiltonian System Identification from Noisy Frequency Response Data

arXiv: https://arxiv.org/abs/2106.11355 · HTML: https://mlsanity.com/paper/2106.11355/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2021.

## Built on by (most cited first, 3 of 3)

- [Data-driven identification of latent port-Hamiltonian systems](https://mlsanity.com/paper/2408.08185/) (2024)
- [Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes](https://mlsanity.com/paper/2406.18726/) (2024)
- [Controlled oscillation modeling using port-Hamiltonian neural networks](https://mlsanity.com/paper/2602.15704/) (2026)

## Abstract (arXiv)

We present a new method for the identification of linear time-invariant passive systems from noisy frequency response data. In particular, we propose to fit a parametrized port-Hamiltonian (pH) system, which is automatically passive, to supplied data with respect to a least-squares objective function. In a numerical study, we assess the accuracy of the resulting identified models by comparing our method to two other frequency domain system identification methods. One of the methods being compared is a recently published identification procedure that also computes pH systems and the other one is the well-known vector-fitting algorithm, which provides unstructured models. The numerical evaluation demonstrates a substantial increase in accuracy of our method compared to the other pH identification procedure and a slightly improved accuracy compared to vector-fitting. This underlines the suitability of our method for the estimation of passive or pH systems - in particular from noisy frequency response data.