1 citations · 2 across the 6 of their papers we have counts for
6 papers
Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis
Nicholas Tagliapietra, Katharina Ensinger, Christoph Zimmer +1
Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics…
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…
Learning Hybrid Dynamics Models With Simulator-Informed Latent States
Katharina Ensinger, Sebastian Ziesche, Sebastian Trimpe
Dynamics model learning deals with the task of inferring unknown dynamics from measurement data and predicting the future behavior of the system. A typical approach to address this…
Exact Inference for Continuous-Time Gaussian Process Dynamics
Katharina Ensinger, Nicholas Tagliapietra, Sebastian Ziesche +1
Physical systems can often be described via a continuous-time dynamical system. In practice, the true system is often unknown and has to be learned from measurement data. Since dat…
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…
Structure-preserving Gaussian Process Dynamics
Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche +2
Most physical processes posses structural properties such as constant energies, volumes, and other invariants over time. When learning models of such dynamical systems, it is criti…