Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
<|reference_start|>Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes: 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 concurrent development of part design, material system and manufacturing process. Current approaches employ numerical simulations, which however quickly becomes computation-intensive, especially for iterative optimization. Data-driven machine learning methods can be used to replace time- and resource-intensive numerical simulations. In particular, MeshGraphNets (MGNs) have shown promising results. They enable fast and accurate predictions on unseen mesh geometries while being fully differentiable for optimization. However, these models rely on large amounts of expensive training data, such as numerical simulations. Physics-informed neural networks (PINNs) offer an opportunity to train neural networks with partial differential equations instead of labeled data, but have not been extended yet to handle time-dependent simulations of arbitrary meshes. This work introduces PI-MGNs, a hybrid approach that combines PINNs and MGNs to quickly and accurately solve non-stationary and nonlinear partial differential equations (PDEs) on arbitrary meshes. The method is exemplified for thermal process simulations of unseen parts with inhomogeneous material distribution. Further results show that the model scales well to large and complex meshes, although it is trained on small generic meshes only.<|reference_end|>
@article{würth2024physics-informed,
    title={Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers
  for non-stationary and nonlinear simulations on arbitrary meshes},
    author={Tobias W"urth, Niklas Freymuth, Clemens Zimmerling, Gerhard Neumann,
  Luise K"arger},
    journal={arXiv preprint arXiv:2402.10681},
    year={2024},
    doi={10.1016/j.cma.2024.117102},
    archivePrefix={arXiv},
    eprint={2402.10681},
    primaryClass={cs.LG cs.AI cs.CE}
}