Structure-preserving identification of port-Hamiltonian systems – a sensitivity-based approach

by   Michael Günther, et al.

We present a gradient-based calibration algorithm to identify a port-Hamiltonian system from given time-domain input-output data. The gradient is computed with the help of sensitivities and the algorithm is tailored such that the structure of the system matrices of the port-Hamiltonian system (skew-symmetry and positive semi-definiteness) is preserved in each iteration of the algorithm. As we only require input-output data, we need to calibrate the initial condition of the internal state of the port-Hamiltonian system as well. Numerical results with synthetic data show the feasibility of the approach.


page 1

page 2

page 3

page 4


Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain

We present a gradient-based calibration algorithm to identify the system...

Discrete gradient methods for irreversible port-Hamiltonian systems

In this paper we introduce discrete gradient methods to discretize irrev...

Hamiltonian Neural Networks with Automatic Symmetry Detection

Recently, Hamiltonian neural networks (HNN) have been introduced to inco...

Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data

We present a numerical approach for approximating unknown Hamiltonian sy...

A Perturbation Scheme for Passivity Verification and Enforcement of Parameterized Macromodels

This paper presents an algorithm for checking and enforcing passivity of...

Operator splitting based dynamic iteration for linear infinite-dimensional port-Hamiltonian systems

A dynamic iteration scheme for linear infinite-dimensional port-Hamilton...

Finding the closest normal structured matrix

Given a structured matrix A we study the problem of finding the closest ...

Please sign up or login with your details

Forgot password? Click here to reset