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20232026
most citedPhysics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes

19 citations · 21 across the 19 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.LG2024

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…

cs.LG2024

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,…

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.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…

cs.LG2024

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…

cs.LG2024

Efficient Off-Policy Learning for High-Dimensional Action Spaces

Fabian Otto, Philipp Becker, Ngo Anh Vien +1

Existing off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation, which can be problematic in high-dimensional action spac…

cs.LG2024★ 19 cited

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…