Veröffentlichungen

Zeitschriftenaufsätze

2024

Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
Würth, T.; Freymuth, N.; Zimmerling, C.; Neumann, G.; Kärger, L.
2024. Computer Methods in Applied Mechanics and Engineering, 429, Artkl.Nr.: 117102. doi:10.1016/j.cma.2024.117102

Swarm reinforcement learning for adaptive mesh refinement
Freymuth, N.; Dahlinger, P.; Würth, T.; Reisch, S.; Kärger, L.; Neumann, G.
2024. Advances in Neural Information Processing Systems, 36 S

2023

Zeiteffiziente und datenfreie Bauteil- und Prozesssimulation mithilfe von Physics-Informed Neural Networks
Würth, T.; Prietze, A.; Zimmerling, C.; Krauß, C.; Kärger, L.
2023. NAFEMS-Magazin, 68 (4), 39–45

Physics-informed neural networks for data-free surrogate modelling and engineering optimization – An example from composite manufacturing
Würth, T.; Krauß, C.; Zimmerling, C.; Kärger, L.
2023. Materials & Design, 231, Art.-Nr.: 112034. doi:10.1016/j.matdes.2023.112034

Forschungsberichte/Preprints

2023

Swarm Reinforcement Learning For Adaptive Mesh Refinement
Freymuth, N.; Dahlinger, P.; Würth, T.; Reisch, S.; Kärger, L.; Neumann, G.
2023. arxiv. doi:10.48550/arXiv.2304.00818