59 citations · 100 across the 22 of their papers we have counts for
5 papers · 1 filter
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…
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…
Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
Tobias Würth, Niklas Freymuth, Clemens Zimmerling +2
Engineering components must meet increasing technological demands in ever shorter development cycles. To face these challenges, a holistic approach is essential that allows for the…