55 citations · 137 across the 38 of their papers we have counts for
6 papers · 1 filter
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-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks
Yuhan Liu, Roland Tóth, Maarten Schoukens
Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical in…
Space-Filling Input Design for Nonlinear State-Space Identification
Máté Kiss, Roland Tóth, Maarten Schoukens
The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing e…
Baseline Results for Selected Nonlinear System Identification Benchmarks
Max D. Champneys, Gerben I. Beintema, Roland Tóth +2
Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting im…
Learning-based model augmentation with LFRs
Jan H. Hoekstra, Chris Verhoek, Roland Tóth +1
Nonlinear system identification (NL-SI) has proven to be effective in obtaining accurate models for highly complex systems. In particular, recent encoder-based methods for artifici…
State Derivative Normalization for Continuous-Time Deep Neural Networks
Jonas Weigand, Gerben I. Beintema, Jonas Ulmen +4
The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper nor…