1k citations · 2k across the 24 of their papers we have counts for
26 papers
Forced-Term Modeling in the Koopman Framework for Linear Operator Learning in Nonlinear Dynamical Systems
Paolo Climaco, Jochen Garcke, Xenia F. Gerloff
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 us…
Diffusion Policies for Short-Horizon Planning in Robot Crowd Navigation
Wendong Li, Jochen Garcke
Robot crowd navigation requires safe and efficient decision-making under dense, dynamic, and multimodal human--robot interactions. Existing reinforcement-learning methods typically…
Accelerating gas-network feasibility screening with a physics-informed graph neural network surrogate
Dongrui Jiang, Jochen Garcke, Okan Akca +4
Large-scale gas-network scenario evaluation is a computational bottleneck in integrated energy-system planning, particularly when gas infrastructure interacts with power, heat, hyd…
Density-Aware Farthest Point Sampling
Paolo Climaco, Jochen Garcke
We focus on training machine learning regression models in scenarios where the availability of labeled training data is limited due to computational constraints or high labeling co…
Learning Crowd Behaviors in Navigation with Attention-based Spatial-Temporal Graphs
Yanying Zhou, Jochen Garcke
Safe and efficient navigation in dynamic environments shared with humans remains an open and challenging task for mobile robots. Previous works have shown the efficacy of using rei…
Unsupervised Representation Learning for Diverse Deformable Shape Collections
Sara Hahner, Souhaib Attaiki, Jochen Garcke +1
We introduce a novel learning-based method for encoding and manipulating 3D surface meshes. Our method is specifically designed to create an interpretable embedding space for defor…