# Research Interests

馃晩 Swarm Reinforcement Learning

馃搻 Graph Neural Networks and Geometric Deep Learning

馃 (Versatile) Reinforcement Learning from Human Feedback

# Publications

Physics-informed MeshGraphNets (PI-MGNs):

Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes *Computer Methods in Applied Mechanics and Engineering* (CMAME), 2024.

T. W眉rth, **N. Freymuth**, C. Zimmerling, G. Neumann, L. K盲rger

Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects. *IEEE Robotics and Automation Letters *(RA鈥慙), 2024.

Scheikl, P. M., Schreiber, N., Haas, C., **Freymuth, N.**, Neumann, G., Lioutikov, R., & Mathis-Ullrich, F.

Swarm Reinforcement Learning for Adaptive Mesh Refinement. *36th Neural Information Processing Systems* (NeurIPS), 2023. **N. Freymuth**, P. Dahlinger, T. W眉rth, S. Reisch, L. K盲rger, G. Neumann

Grounding Graph Network Simulators using Physical Sensor Observations. *11th International Conference on Learning Representations* (ICLR), 2023.

J. Linkerh盲gner, **N. Freymuth**, P.M. Scheikl, F. Mathis-Ullrich, G. Neumann

Adversarial Imitation Learning with Preferences. *11th International Conference on Learning Representations* (ICLR), 2023.

A. Taranovic, A.G. Kupcsik, **N. Freymuth**, G. Neumann

Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors. *6th Conference on Robot Learning* (CoRL), 2022. **N. Freymuth**, N. Schreiber, P. Becker, A. Taranovic, G. Neumann

### Workshops

KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty. *“Next Generation of Sequence Modeling Architectures” and “Aligning Reinforcement Learning Experimentalists and Theorists” Workshops at International Conference on Machine Learning (ICML)*, 2024. P. Becker, **N. Freymuth**, G. Neumann

Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations. *AI4Science Workshop at International Conference on Machine Learning (ICML)*, 2024.**N. Freymuth**, P. Dahlinger, T. W眉rth, P. Becker, A. Taranovic, O. Gr枚nheim, L. K盲rger, G. Neumann

Latent Task-Specific Graph Network Simulators. *AI4Science Workshop at Neural Information Processing Systems* (NeurIPS), 2023.

P. Dahlinger, **N. Freymuth**, M. Volpp, T. Hoang, G. Neumann

Swarm Reinforcement Learning for Adaptive Mesh Refinement (v1). *Workshop on Physics for Machine Learning at International Conference on Learning Representations (ICLR)*, 2023. **N. Freymuth**, P. Dahlinger, T. W眉rth, L. K盲rger, G. Neumann

Versatile inverse reinforcement learning via cumulative rewards. *4th Robot Learning Workshop at Neural Information Processing Systems* (NeurIPS), 2021. **N. Freymuth**, P. Becker, and G. Neumann

# Theses Supervision and Teaching

### Bachelor’s Theses

- Autonomous Partitioning of Heterogeneous Swarm Systems
- Episodic Versatile Imitation Learning via Geometric Feature Matching
- Representation-Invariant Latent Spaces for Imitation Learning from Observation
- Swarm Reinforcement Learning with Graph Neural Networks

### Teaching

- Teaching Assistant for the “Reinforcement Learning” Lecture @ KIT
- Seminar and Praktikum “Robot Learning” @ KIT

### Misc. Projects

- Efficient Swarm Reinforcement Learning from Human Feedback
- Spatial Credit Assignment for Swarm Reinforcement Learning

### Master’s Theses

- Adaptive Mesh HP-Refinement with Swarm Reinforcement Learning
- Explainable Graph Neural Networks for Autonomous Driving
- Explainable Graph Neural Networks for Traffic Routing
- Graph Diffusion for Adaptive Mesh R-Refinement
- Adaptive Mesh Refinement from Human Feedback
- Variational Swarm Reinforcement Learning for Predictive Models of Deformable Objects
- Reinforcement Learning for Deformable Object Manipulation
- Swarm Reinforcement Learning in Limited Visibility with Graph Neural Networks
- Physical Simulation from Observation using Graph Neural Networks
- RippleNet: A remote message passing method for Graph Neural Networks