5 papers
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…
Data-based Moving Horizon Estimation under Irregularly Measured Data
Tobias M. Wolff, Isabelle Krauss, Victor G. Lopez +1
In this work, we introduce a sample- and data-based moving horizon estimation framework for linear systems. We perform state estimation in a sample-based fashion in the sense that…
Estimating Hormone Concentrations in the Pituitary-Thyroid Feedback Loop from Irregularly Sampled Measurements
Seth Siriya, Tobias M. Wolff, Isabelle Krauss +2
Model-based control techniques have recently been investigated for the recommendation of medication dosages to address thyroid diseases. These techniques often rely on knowledge of…
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller
This paper is centered around the approximation of dynamical systems by means of Gaussian processes. To this end, trajectories of such systems must be collected to be used as train…
Gaussian Process-Based Nonlinear Moving Horizon Estimation
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller
In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. On the one hand, we approximate the system dynamic…