1 citations · 1 across the 6 of their papers we have counts for
6 papers
Safe Active Learning for Gaussian Differential Equations
Leon Glass, Katharina Ensinger, Christoph Zimmer
Gaussian Process differential equations (GPODE) have recently gained momentum due to their ability to capture dynamics behavior of systems and also represent uncertainty in predict…
Batch Active Learning in Gaussian Process Regression using Derivatives
Hon Sum Alec Yu, Christoph Zimmer, Duy Nguyen-Tuong
We investigate the use of derivative information for Batch Active Learning in Gaussian Process regression models. The proposed approach employs the predictive covariance matrix for…
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Christoph Zimmer, Mona Meister, Duy Nguyen-Tuong
Learning time-series models is useful for many applications, such as simulation and forecasting. In this study, we consider the problem of actively learning time-series models whil…
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…
Super-localised wave function approximation of Bose-Einstein condensates
Daniel Peterseim, Johan Wärnegård, Christoph Zimmer
This paper presents a novel spatial discretisation method for the reliable and efficient simulation of Bose-Einstein condensates modelled by the Gross-Pitaevskii equation and the c…
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems
Matthias Bitzer, Mona Meister, Christoph Zimmer
Learning precise surrogate models of complex computer simulations and physical machines often require long-lasting or expensive experiments. Furthermore, the modeled physical depen…