| Physics-informed neural networks for data-free surrogate modelling and engineering optimization–an example from composite manufacturing T Würth, C Krauß, C Zimmerling, L Kärger Materials & Design 231, 112034, 2023 | 13 | 2023 |
| Swarm reinforcement learning for adaptive mesh refinement N Freymuth, P Dahlinger, T Würth, S Reisch, L Kärger, G Neumann Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes T Würth, N Freymuth, C Zimmerling, G Neumann, L Kärger Computer Methods in Applied Mechanics and Engineering 429, 117102, 2024 | 1 | 2024 |
| Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations N Freymuth, P Dahlinger, T Würth, P Becker, A Taranovic, O Grönheim, ... arXiv preprint arXiv:2406.14161, 2024 | 1 | 2024 |
| Adaptive Swarm Mesh Refinement using Deep Reinforcement Learning with Local Rewards N Freymuth, P Dahlinger, T Würth, S Reisch, L Kärger, G Neumann arXiv preprint arXiv:2406.08440, 2024 | | 2024 |
| Zeiteffiziente und datenfreie Bauteil-und Prozesssimulation mithilfe von Physics-Informed Neural Networks T Würth, A Prietze, C Zimmerling, C Krauß, L Kärger NAFEMS-Magazin 68 (4), 39, 2023 | | 2023 |
