Learning ODE Models with Qualitative Structure Using Gaussian Processes. - Steffen Ridderbusch; Paul Goulart; Sina Ober-Blöbaum (CoRR, 2020)

Learning ODE Models with Qualitative Structure Using Gaussian Processes. - Steffen Ridderbusch; Christian Offen; Sina Ober-Blöbaum; Paul Goulart (CDC, 2021)

Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery. - Yana Lishkova; Paul Scherer; Steffen Ridderbusch; Mateja Jamnik; Pietro Liò; Sina Ober-Blöbaum; Christian Offen (CoRR, 2022)

Approximate Uncertainty Propagation for Continuous Gaussian Process Dynamical Systems. - Steffen Ridderbusch; Sina Ober-Blöbaum; Paul Goulart (CoRR, 2022)

The past does matter: correlation of subsequent states in trajectory predictions of Gaussian Process models. - Steffen Ridderbusch; Sina Ober-Blöbaum; Paul Goulart (UAI, 2023)

Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers. - Tyler Sours; Shivang Agarwal; Marc Cormier; Jordan Crivelli-Decker; Steffen Ridderbusch; Stephen L. Glazier; Connor P. Aiken; Aayush R. Singh; Ang Xiao; Omar Allam (CoRR, 2024)

Probabilistic Pontryagin's Maximum Principle for Continuous-Time Model-Based Reinforcement Learning. - David Leeftink; Çagatay Yildiz; Steffen Ridderbusch; Max Hinne; Marcel van Gerven (CoRR, 2025)