3 papers
eess.SY2026
Improved Initialization for Port-Hamiltonian Neural Network Models
G. J. E. van Otterdijk, S. Weiland, M. Schoukens
Port-Hamiltonian neural networks have shown promising results in the identification of nonlinear dynamics of complex systems, as their combination of physical principles with data-…
eess.SY2026
Identification of Port-Hamiltonian Differential-Algebraic Equations from Input-Output Data
N. Hagelaars, G. J. E. van Otterdijk, S. Moradi +3
Many models of physical systems, such as mechanical and electrical networks, exhibit algebraic constraints that arise from subsystem interconnections and underlying physical laws.…
eess.SY2024
Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
G. J. E. van Otterdijk, S. Moradi, S. Weiland +3
Port-Hamiltonian neural networks (pHNNs) are emerging as a powerful modeling tool that integrates physical laws with deep learning techniques. While most research has focused on mo…