Tobias Würth, M.Sc.
- Machine learning and AI-based process optimization
- Machine learning and AI-supported process optimization
- Research Group:
Umform- und Struktursimulation
- Room: 106
CO 70.04 - Phone: +49 721 608-45410
- tobias wuerth ∂does-not-exist.kit edu
- Rintheimer Queralle 2
76131 Karlsruhe
Publications
Journal Articles
2026
Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning
Dahlinger, P.; Freymuth, N.; Hoang, T.; Würth, T.; Volpp, M.; Kärger, L.; Neumann, G.
2026. Transactions on Machine Learning Research, 2026-January, 1
Dahlinger, P.; Freymuth, N.; Hoang, T.; Würth, T.; Volpp, M.; Kärger, L.; Neumann, G.
2026. Transactions on Machine Learning Research, 2026-January, 1
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
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
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
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
Würth, T.; Krauß, C.; Zimmerling, C.; Kärger, L.
2023. Materials & Design, 231, Art.-Nr.: 112034. doi:10.1016/j.matdes.2023.112034
Conference Papers
2025
Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics
Würth, T.; Freymuth, N.; Neumann, G.; Kärger, L.
2025. The 39th Annual Conference on Neural Information Processing Systems (NeurlPS 2025), 30 S., Neural information processing systems foundation
Würth, T.; Freymuth, N.; Neumann, G.; Kärger, L.
2025. The 39th Annual Conference on Neural Information Processing Systems (NeurlPS 2025), 30 S., Neural information processing systems foundation
Reports/Preprints
2026
Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning
Dahlinger, P.; Freymuth, N.; Hoang, T.; Würth, T.; Volpp, M.; Kärger, L.; Neumann, G.
2026. arxiv. doi:10.48550/arXiv.2511.05234
Dahlinger, P.; Freymuth, N.; Hoang, T.; Würth, T.; Volpp, M.; Kärger, L.; Neumann, G.
2026. arxiv. doi:10.48550/arXiv.2511.05234
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
Freymuth, N.; Dahlinger, P.; Würth, T.; Reisch, S.; Kärger, L.; Neumann, G.
2023. arxiv. doi:10.48550/arXiv.2304.00818