18 citations · 18 across the 4 of their papers we have counts for
4 papers
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification
Thomas Beckers, Tom Z. Jiahao, George J. Pappas
Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes. The dy…
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
Thomas Beckers, Jacob Seidman, Paris Perdikaris +1
Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which…
Physics-enhanced Gaussian Process Variational Autoencoder
Thomas Beckers, Qirui Wu, George J. Pappas
Variational autoencoders allow to learn a lower-dimensional latent space based on high-dimensional input/output data. Using video clips as input data, the encoder may be used to de…
Learning Rigidity-based Flocking Control with Gaussian Processes
Manuela Gamonal, Thomas Beckers, George J. Pappas +1
Flocking control of multi-agents system is challenging for agents with partially unknown dynamics. This paper proposes an online learning-based controller to stabilize flocking mot…