activity
20222026
most citedPhysics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes

19 citations · 36 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

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…

physics.class-ph2025

Capabilities and limitations of pure-shear based macroscopic forming simulations for 0{\textdegree}/90{\textdegree} biaxial non-crimp fabrics

Bastian Schäfer, Dominik Dörr, Naim Naouar +2

Macroscopic modeling of a non-crimp fabric's (NCF's) forming behavior is challenging as it strongly depends on the textile architecture, fiber type, and stitching type. While shear…

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★ 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…

cs.MA2023★ 9 cited

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…

cs.CE2022★ 6 cited

Non-isothermal direct bundle simulation of SMC compression molding with a non-Newtonian compressible matrix

Nils Meyer, Sergej Ilinzeer, Andrew N. Hrymak +2

Compression molding of Sheet Molding Compounds (SMC) is a manufacturing process in which a stack of discontinuous fiber-reinforced thermoset sheets is formed in a hot mold. The reo…