Domain-Decoupled Physics-informed Neural Networks with Closed-Form Gradients for Fast Model Learning of Dynamical Systems. - Henrik Krauss; Tim-Lukas Habich; Max Bartholdt; Thomas Seel; Moritz Schappler (ICINCO (1), 2024)

Enhanced Model-Free Dynamic State Estimation for a Soft Robot Finger Using an Embedded Optical Waveguide Sensor. - Henrik Krauss; Kenjiro Takemura (IEEE Robotics Autom. Lett., 2024)

Revealing Covert Attention by Analyzing Human and Reinforcement Learning Agent Gameplay. - Henrik Krauss; Takehisa Yairi (CoRR, 2025)

Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory. - Johann Licher; Max Bartholdt; Henrik Krauss; Tim-Lukas Habich; Thomas Seel; Moritz Schappler (CoRR, 2025)