4 papers
Stochastic Port-Hamiltonian Neural Networks: Universal Approximation with Passivity Guarantees
Luca Di Persio, Matthias Ehrhardt, Youness Outaleb
Stochastic port-Hamiltonian systems represent open dynamical systems with dissipation, inputs, and stochastic forcing in an energy based form. We introduce stochastic port-Hamilton…
Modelling car-following dynamics with stochastic input-state-output port-Hamiltonian systems
Julia Ackermann, Matthias Ehrhardt, Thomas Kruse +1
In this contribution, we introduce a general class of car-following models with an input-state-output port-Hamiltonian structure. We derive stability conditions and long-term behav…
Stability of heterogeneous linear and nonlinear car-following models
Matthias Ehrhardt, Antoine Tordeux
Stop-and-go waves in road traffic are complex collective phenomena with significant implications for traffic engineering, safety and the environment. Despite decades of research, u…
Stabilisation of stochastic single-file dynamics using port-Hamiltonian systems
Julia Ackermann, Matthias Ehrhardt, Thomas Kruse +1
This study revisits a recently proposed symmetric port-Hamiltonian single-file model in one dimension. The uniform streaming solutions are stable in the deterministic model. Howeve…