1 citations · 1 across the 2 of their papers we have counts for
3 papers
Uncertainty and Structure in Neural Ordinary Differential Equations
Katharina Ott, Michael Tiemann, Philipp Hennig
Neural ordinary differential equations (ODEs) are an emerging class of deep learning models for dynamical systems. They are particularly useful for learning an ODE vector field fro…
Bayesian Numerical Integration with Neural Networks
Katharina Ott, Michael Tiemann, Philipp Hennig +1
Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the in…
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch +2
Modeling an unknown dynamical system is crucial in order to predict the future behavior of the system. A standard approach is training recurrent models on measurement data. While t…