19 citations · 30 across the 4 of their papers we have counts for
5 papers
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…
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…
Swarm Reinforcement Learning For Adaptive Mesh Refinement
Niklas Freymuth, Philipp Dahlinger, Tobias Würth +3
Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enab…