From Structured to Unstructured:A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs
<|reference_start|>From Structured to Unstructured:A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs: This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three data\-sets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.<|reference_end|>
@article{decke2024from,
    title={From Structured to Unstructured:A Comparative Analysis of Computer
  Vision and Graph Models in solving Mesh-based PDEs},
    author={Jens Decke, Olaf W"unsch, Bernhard Sick, Christian Gruhl},
    journal={arXiv preprint arXiv:2406.00081},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00081},
    primaryClass={cs.LG cs.CE cs.CV}
}