3 citations · 4 across the 5 of their papers we have counts for
6 papers
Physics-informed Gaussian Processes as Linear Model Predictive Controller with Constraint Satisfaction
Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann
Model Predictive Control evolved as the state of the art paradigm for safety critical control tasks. Control-as-Inference approaches thereof model the constrained optimization prob…
Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems
Adrian Lepp, Jörn Tebbe, Andreas Besginow
Recently, a novel linear model predictive control algorithm based on a physics-informed Gaussian Process has been introduced, whose realizations strictly follow a system of underly…
Linear ordinary differential equations constrained Gaussian Processes for solving optimal control problems
Andreas Besginow, Markus Lange-Hegermann, Jörn Tebbe
This paper presents an intrinsic approach for addressing control problems with systems governed by linear ordinary differential equations (ODEs). We use computer algebra to constra…
Physics-informed Gaussian Processes as Linear Model Predictive Controller
Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann
We introduce a novel algorithm for controlling linear time invariant systems in a tracking problem. The controller is based on a Gaussian Process (GP) whose realizations satisfy a…
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
Andreas Besginow, Jan David Hüwel, Thomas Pawellek +2
Model selection aims to find the best model in terms of accuracy, interpretability or simplicity, preferably all at once. In this work, we focus on evaluating model performance of…
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations
Andreas Besginow, Markus Lange-Hegermann
Data in many applications follows systems of Ordinary Differential Equations (ODEs). This paper presents a novel algorithmic and symbolic construction for covariance functions of G…