DDPS | Physics-enhanced Gaussian Processes for Learning of Electromechanical Systems |Thomas Beckers

Data-driven approaches achieve remarkable results for modeling nonlinear electromechanical systems based on collected data. However, these models often neglect basic physical principles which determine the behavior of any real-world system. This omission is unfavorable in two ways: The models are not as data-efficient as they could be by incorporating physical prior knowledge, and the model itself might not be physically correct.

In this talk, I will present our results on physics-enhanced Gaussian processes for learning of dynamical system with a focus on the class of electromechanical systems. First, I will propose Gaussian Process Port-Hamiltonian systems (GP-PHS) as a physics-informed, nonparametric Bayesian learning approach with uncertainty quantification. In contrast to many physics-informed techniques that impose physics by penalty, the proposed data-driven model is physically correct by design. The Bayesian nature of GP-PHS uses collected data to form a distribution over all possible Port-Hamiltonian systems instead of a single point estimate. Second, I will present our recent results on physics-enhanced variational autoencoders that make use of a physically enhanced Gaussian process prior on the latent dynamics to improve its efficiency and to allow physically correct predictions. The physical prior knowledge expressed as a linear dynamical system is here reflected by the Green's function and included in the kernel function of the Gaussian process. The benefits of the proposed approach are highlighted in a simulation with an oscillating particle.

Bio: Thomas Beckers is an Assistant Professor of Computer Science and the Institute for Software Integrated Systems at Vanderbilt University. Before joining Vanderbilt, he was a postdoctoral researcher at the Department of Electrical and Systems Engineering, University of Pennsylvania, where he was member of the GRASP Lab, PRECISE & ASSET Center. In 2020, he earned his doctorate in Electrical Engineering at the Technical University of Munich (TUM), Germany. He received the B.Sc. and M.Sc. degree in Electrical Engineering in 2010 and 2013, respectively, from the Technical University of Braunschweig, Germany. In 2018, he was a visiting researcher at the University of California, Berkeley. He is a DAAD AInet fellow and was awarded with the Rhode & Schwarz Outstanding Dissertation prize. His research interests include physics-enhanced learning, nonparametric models, and safe learning-based control (www.tbeckers.com).

LLNL-VIDEO-851990