5 papers
Gray-Box Optimization using Optimism in the Face of Uncertainty
Katrin Baumgärtner, Léo Simpson, Moritz Diehl
This paper considers sequential gray-box optimization where the objective function is given as the composition of a loss function and a parametric model. Crucially, the parameters…
Finite-sample bounds for multi-output system identification
Léo Simpson, Katrin Baumgärtner, Johannes Köhler +1
This paper presents uniform-in-time error bounds for regularized linear regression with vector-valued outputs and conditionally zero-mean subgaussian noise. By revisitileveraging s…
Identification of a Kalman filter: consistency of local solutions
Léo Simpson, Moritz Diehl
Prediction error and maximum likelihood methods are powerful tools for identifying linear dynamical systems and, in particular, enable the joint estimation of model parameters and…
Parallelizable Parametric Nonlinear System Identification via tuning of a Moving Horizon State Estimator
Léo Simpson, Jonas Asprion, Simon Muntwiler +2
This paper introduces a novel optimization-based approach for parametric nonlinear system identification. Building upon the prediction error method framework, traditionally used fo…
A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units
Jing Xie, Léo Simpson, Jonas Asprion +1
Temperature control is a complex task due to its often unknown dynamics and disturbances. This paper explores the use of Neural Nonlinear AutoRegressive eXogenous (NNARX) models fo…