Efficiently parallelizable kernel-based multi-scale algorithm
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910862431748096 |
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| author | Lot, Federico Rieger, Christian |
| author_facet | Lot, Federico Rieger, Christian |
| contents | The kernel-based multi-scale method has been proven to be a powerful approximation method for scattered data approximation problems which is computationally superior to conventional kernel-based interpolation techniques. The multi-scale method is based of an hierarchy of point clouds and compactly supported radial basis functions, typically Wendland functions. There is a rich body of literature concerning the analysis of this method including error estimates. This article addresses the efficient parallelizable implementation of those methods. To this end, we present and analyse a monolithic approach to compute the kernel-based multi-scale approximation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04914 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficiently parallelizable kernel-based multi-scale algorithm Lot, Federico Rieger, Christian Numerical Analysis The kernel-based multi-scale method has been proven to be a powerful approximation method for scattered data approximation problems which is computationally superior to conventional kernel-based interpolation techniques. The multi-scale method is based of an hierarchy of point clouds and compactly supported radial basis functions, typically Wendland functions. There is a rich body of literature concerning the analysis of this method including error estimates. This article addresses the efficient parallelizable implementation of those methods. To this end, we present and analyse a monolithic approach to compute the kernel-based multi-scale approximation. |
| title | Efficiently parallelizable kernel-based multi-scale algorithm |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2503.04914 |