activity
20232025
most citedIterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2025

Hierarchical Multi-field Representations for Two-Stage E-commerce Retrieval

Niklas Freymuth, Dong Liu, Thomas Ricatte +1

Dense retrieval methods typically target unstructured text data represented as flat strings. However, e-commerce catalogs often include structured information across multiple field…

cs.LG2024

KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty

Philipp Becker, Niklas Freymuth, Gerhard Neumann

Probabilistic State Space Models (SSMs) are essential for Reinforcement Learning (RL) from high-dimensional, partial information as they provide concise representations for control…

cs.LG20242 cited

Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations

Niklas Freymuth, Philipp Dahlinger, Tobias Würth +5

Many engineering systems require accurate simulations of complex physical systems. Yet, analytical solutions are only available for simple problems, necessitating numerical approxi…

cs.LG2023

Latent Task-Specific Graph Network Simulators

Philipp Dahlinger, Niklas Freymuth, Michael Volpp +2

Simulating dynamic physical interactions is a critical challenge across multiple scientific domains, with applications ranging from robotics to material science. For mesh-based sim…

cs.LG20231 cited

Grounding Graph Network Simulators using Physical Sensor Observations

Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl +2

Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned…