74 citations · 75 across the 9 of their papers we have counts for
3 papers · 1 filter
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.…
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…
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models
Sarvin Moradi, Nick Jaensson, Roland Tóth +1
In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neura…