3 papers
eess.SY2025
Inference in Latent Force Models Using Optimal State Estimation
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller +1
Latent force models, a class of hybrid modeling approaches, integrate physical knowledge of system dynamics with a latent force - an unknown, unmeasurable input modeled as a Gaussi…
eess.SY2025
Physics-informed Learning for Passivity-based Tracking Control
Thomas Beckers, Leonardo Colombo
Passivity-based control ensures system stability by leveraging dissipative properties and is widely applied in electrical and mechanical systems. Port-Hamiltonian systems (PHS), in…
eess.SY2025
Safe Physics-Informed Machine Learning for Dynamics and Control
Jan Drgona, Truong X. Nghiem, Thomas Beckers +6
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models…