6 papers
Target Parameterization in Diffusion Models for Nonlinear Spatiotemporal System Identification
Achraf El Messaoudi, Noureddine Khaous, Karim Cherifi
Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed outputs. However, classical identifi…
PH-KAN: Port-Hamiltonian Kolmogorov-Arnold Network
Achraf El Messaoudi, Karim Cherifi, Yann Le Gorrec +1
Data-driven machine learning approaches have become increasingly attractive for nonlinear system identification, but standard models often fail to preserve the underlying physical…
Neural Scaling Laws for Learning-based Identification of Nonlinear Systems
Marco Roschkowski, Karim Cherifi, Hannes Gernandt
The use of machine learning models in system identification has increased due to their ability to approximate complex nonlinear dynamics with high accuracy. However, often it is no…
Nonlinear port-Hamiltonian system identification from input-state-output data (ISO-pHNN)
Karim Cherifi, Achraf El Messaoudi, Hannes Gernandt +1
In this paper, we introduce a framework called ISO-pHNN for identifying nonlinear port-Hamiltonian systems using input-state-output data. The framework utilizes neural networks' un…
Finding the nearest bounded-real port-Hamiltonian system
Karim Cherifi, Nicolas Gillis, Punit Sharma
In this paper, we consider linear time-invariant continuous control systems which are bounded real, also known as scattering passive. Our main theoretical contribution is to show t…
Relationship between dissipativity concepts for linear time-varying port-Hamiltonian systems
Karim Cherifi, Hannes Gernandt, Dorothea Hinsen +2
The relationship between different dissipativity concepts for linear time-varying systems is studied, in particular between port-Hamiltonian systems, passive systems, and systems w…