2 papers
stat.ML2025
Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge
Björn Volkmann, Jan-Hendrik Ewering, Michael Meindl +2
Accuracy and generalization capabilities are key objectives when learning dynamical system models. To obtain such models from limited data, current works exploit prior knowledge an…
eess.SY2024
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline
Jan-Hendrik Ewering, Björn Volkmann, Simon F. G. Ehlers +2
Intelligent real-world systems critically depend on expressive information about their system state and changing operation conditions, e.g., due to variation in temperature, locati…