From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs
J. Decke, O. Wünsch, B. Sick, and C. Gruhl. International Conference on Architecture of Computing Systems (ARCS), Springer, (2024)(accepted).
Abstract
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 datasets. 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.
%0 Conference Paper
%1 decke2024structured
%A Decke, Jens
%A Wünsch, Olaf
%A Sick, Bernhard
%A Gruhl, Christian
%B International Conference on Architecture of Computing Systems (ARCS)
%D 2024
%I Springer
%K imported itegpub isac-www Organic Computing Self-Optimization DeepLearning PartialDifferentialEquation SurrogateModel MeshTopographies
%T From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs
%X 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 datasets. 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.
@inproceedings{decke2024structured,
abstract = {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 datasets. 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.},
added-at = {2024-04-11T14:52:52.000+0200},
author = {Decke, Jens and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian},
biburl = {https://www.bibsonomy.org/bibtex/2bf3baf1e39a1493c9cf4fcb18c6a9334/ies},
booktitle = {International Conference on Architecture of Computing Systems (ARCS)},
interhash = {ed6bd9576bb4b4cfbbce236485b92f99},
intrahash = {bf3baf1e39a1493c9cf4fcb18c6a9334},
keywords = {imported itegpub isac-www Organic Computing Self-Optimization DeepLearning PartialDifferentialEquation SurrogateModel MeshTopographies},
note = {(accepted)},
publisher = {Springer},
timestamp = {2024-04-11T14:52:52.000+0200},
title = {From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs},
year = 2024
}