Reordering CAE Matrix using Hierarchical Clustering

He Shanhong, Yang Yahui, Tang Lihong, Zhang Tao, Jiang Xiaolong
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:413-420, 2024.

Abstract

Traversing a high-dimensional mesh using 1-D trajectory is a widely used technique in the fields of CAE computing and data management. Such a trajectory is widely known as a space-filling curve. Nevertheless, most of the space filling curves, such as Z-curve or Hilbert curve, are designed for structured meshes. Therefore, it is vital to design an effective space-filling curve for unstructured meshes. In this paper, we propose a space-filling curve for unstructured mesh by considering the original problem as a graph clustering problem. We generate a hierarchical clustering schema and uses depth-first search to traverse the hierarchical schema. In this way, the sequence of the depth-first search naturally be-comes a 1-D trajectory. Compared with traditional space-filling curves, our solution can effectively handle traversing problems on unstructured meshes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-shanhong24a, title = {Reordering CAE Matrix using Hierarchical Clustering}, author = {Shanhong, He and Yahui, Yang and Lihong, Tang and Tao, Zhang and Xiaolong, Jiang}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {413--420}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/shanhong24a/shanhong24a.pdf}, url = {https://proceedings.mlr.press/v245/shanhong24a.html}, abstract = {Traversing a high-dimensional mesh using 1-D trajectory is a widely used technique in the fields of CAE computing and data management. Such a trajectory is widely known as a space-filling curve. Nevertheless, most of the space filling curves, such as Z-curve or Hilbert curve, are designed for structured meshes. Therefore, it is vital to design an effective space-filling curve for unstructured meshes. In this paper, we propose a space-filling curve for unstructured mesh by considering the original problem as a graph clustering problem. We generate a hierarchical clustering schema and uses depth-first search to traverse the hierarchical schema. In this way, the sequence of the depth-first search naturally be-comes a 1-D trajectory. Compared with traditional space-filling curves, our solution can effectively handle traversing problems on unstructured meshes.} }
Endnote
%0 Conference Paper %T Reordering CAE Matrix using Hierarchical Clustering %A He Shanhong %A Yang Yahui %A Tang Lihong %A Zhang Tao %A Jiang Xiaolong %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-shanhong24a %I PMLR %P 413--420 %U https://proceedings.mlr.press/v245/shanhong24a.html %V 245 %X Traversing a high-dimensional mesh using 1-D trajectory is a widely used technique in the fields of CAE computing and data management. Such a trajectory is widely known as a space-filling curve. Nevertheless, most of the space filling curves, such as Z-curve or Hilbert curve, are designed for structured meshes. Therefore, it is vital to design an effective space-filling curve for unstructured meshes. In this paper, we propose a space-filling curve for unstructured mesh by considering the original problem as a graph clustering problem. We generate a hierarchical clustering schema and uses depth-first search to traverse the hierarchical schema. In this way, the sequence of the depth-first search naturally be-comes a 1-D trajectory. Compared with traditional space-filling curves, our solution can effectively handle traversing problems on unstructured meshes.
APA
Shanhong, H., Yahui, Y., Lihong, T., Tao, Z. & Xiaolong, J.. (2024). Reordering CAE Matrix using Hierarchical Clustering. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:413-420 Available from https://proceedings.mlr.press/v245/shanhong24a.html.

Related Material