Gerhard Neumann

Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators

Philipp Dahlinger, Balázs Gyenes, Niklas Freymuth +6

Graph Network Simulators (GNSs) have emerged as powerful surrogates for complex physics-based simulation, offering inherent differentiability and orders-of-magnitude speedups over…

Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms

Andreas Boltres, Niklas Freymuth, Benjamin Schichtholz +2

Routing algorithms are crucial for efficient computer network operations, and in many settings they must be able to react to traffic bursts within milliseconds. Live telemetry data…

SEAR: Sample Efficient Action Chunking Reinforcement Learning

C. F. Maximilian Nagy, Onur Celik, Emiliyan Gospodinov +4

Action chunking improves exploration and accelerates value propagation in long-horizon reinforcement learning, but naively applying off-policy methods to the temporally extended ac…

Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching

Denis Blessing, Lorenz Richter, Julius Berner +2

Sampling from unnormalized densities using diffusion models has emerged as a powerful paradigm. However, while recent approaches that use least-squares `matching' objectives have i…

Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?

Andreas Boltres, Niklas Freymuth, Gerhard Neumann

Telemetry-Aware routing promises to increase efficacy and responsiveness to traffic surges in computer networks. Recent research leverages Machine Learning to deal with the complex…

eess.SY2026

Smooth Sampling-Based Model Predictive Control Using Deterministic Samples

Markus Walker, Marcel Reith-Braun, Tai Hoang +2

Sampling-based model predictive control (MPC) is effective for nonlinear systems but often produces non-smooth control inputs due to random sampling. To address this issue, we exte…

Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics

Tai Hoang, Alessandro Trenta, Alessio Gravina +4

Learning to simulate complex physical systems from data has emerged as a promising way to overcome the limitations of traditional numerical solvers, which often require prohibitive…

cs.RO2025

Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning

Philipp Dahlinger, Niklas Freymuth, Tai Hoang +4

Simulating object deformations is a critical challenge across many scientific domains, including robotics, manufacturing, and structural mechanics. Learned Graph Network Simulators…

MaNGO - Adaptable Graph Network Simulators via Meta-Learning

Philipp Dahlinger, Tai Hoang, Denis Blessing +2

Accurately simulating physics is crucial across scientific domains, with applications spanning from robotics to materials science. While traditional mesh-based simulations are prec…

TROLL: Trust Regions improve Reinforcement Learning for Large Language Models

Philipp Becker, Niklas Freymuth, Serge Thilges +2

Reinforcement Learning (RL) with PPO-like clip objectives has become the standard choice for reward-based fine-tuning of large language models (LLMs). Although recent work has expl…

Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics

Tobias Würth, Niklas Freymuth, Gerhard Neumann +1

Graph-based learned simulators have emerged as a promising approach for simulating physical systems on unstructured meshes, offering speed and generalization across diverse geometr…

AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction

Niklas Freymuth, Tobias Würth, Nicolas Schreiber +9

The cost and accuracy of simulating complex physical systems using the Finite Element Method (FEM) scales with the resolution of the underlying mesh. Adaptive meshes improve comput…

Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects

Tai Hoang, Huy Le, Philipp Becker +2

Manipulating objects with varying geometries and deformable objects is a major challenge in robotics. Tasks such as insertion with different objects or cloth hanging require precis…

PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds

Balázs Gyenes, Nikolai Franke, Philipp Becker +1

Perceiving the environment via cameras is crucial for Reinforcement Learning (RL) in robotics. While images are a convenient form of representation, they often complicate extractin…

Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics

Andreas Boltres, Niklas Freymuth, Patrick Jahnke +2

Finding efficient routes for data packets is an essential task in computer networking. The optimal routes depend greatly on the current network topology, state and traffic demand,…

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.LG2024★ 2 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…

Adaptive Swarm Mesh Refinement using Deep Reinforcement Learning with Local Rewards

Niklas Freymuth, Philipp Dahlinger, Tobias Würth +3

Simulating physical systems is essential in engineering, but analytical solutions are limited to straightforward problems. Consequently, numerical methods like the Finite Element M…