3 papers
math.OC2025
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…
math.OC2025
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…
cs.LG2024
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Jörn Tebbe, Christoph Zimmer, Ansgar Steland +2
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their cali…