Author

J. Garcke

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Preprint Sep 2026

Forced-Term Modeling in the Koopman Framework for Linear Operator Learning in Nonlinear Dynamical Systems

We study the problem of extracting reliable and interpretable linear models from nonlinear dynamical systems with periodic behavior. Dynamic Mode Decomposition (DMD) is a widely used data-driven method for approximating dynamics by a linear system and is commonly interpreted through Koopman operator theory. However, be...

Paolo Climaco, J. Garcke, Xenia F. Gerloff · 0 citations

Preprint Aug 2026

Diffusion Policies for Short-Horizon Planning in Robot Crowd Navigation

Robot crowd navigation requires safe and efficient decision-making under dense, dynamic, and multimodal human--robot interactions. Existing reinforcement-learning methods typically output a single reactive action at each timestep, which limits their ability to represent diverse short-term avoidance strategies. We propo...

Wen-Dong Li, J. Garcke · 0 citations