Modeling, simulation and code-generation of multiphysical Port-Hamiltonian Systems in Python.

Fault-tolerant formation control using energy tanks

Port-Hamiltonian Approach to Neural Network Training

The package phlearn for modelling pseudo-Hamiltonian systems by pseudo-Hamiltonian neural networks (PHNN), for ODEs and PDEs

A collection of port-Hamiltonian benchmark systems

MORpH - Model reduction of port-Hamiltonian systems

Port-Hamiltonian Modeling of 3D Truss Structures and Frames

A compositional, energy-based software framework for modeling mechanical, electromagnetic and thermodynamic systems

Python code and Jupyter notebooks for producing the simulation results of my master's thesis.

Based on my master's thesis on synchronization of two coupled 5D Hindmarsh-Rose neurons. Using port-Hamiltonian Physics Informed Neural Network (pH-PINN) to predict Hamiltonian energy of synchronization dynamics of coupled neurons based on trajectory data. We employed analytical solution on Hamiltonian energy to validate the pH-PINN results.

pydykit: A Python-based dynamics simulation toolkit.

Simulation results of "Kinon, P. L., Eugster, S. R. & Betsch, P. (2026): Mixed formulation and structure-preserving discretization of Cosserat rod dynamics in a port-Hamiltonian framework"

Code repository for "Kinon, P. L., Morandin, R. & Schulze, P. (2025): Discrete gradient methods for port-Hamiltonian differential-algebraic equations."

Port-Hamiltonian systems in Julia

Model reduction for port-Hamiltonian and passive systems in Julia

Port-Hamiltonian Model for Gas Networks with Compressors (mirror repository)