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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2408.13193 |
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| _version_ | 1866913477868650496 |
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| author | Ma, Guanqun Lenz, David Peterka, Tom Guo, Hanqi Wang, Bei |
| author_facet | Ma, Guanqun Lenz, David Peterka, Tom Guo, Hanqi Wang, Bei |
| contents | Advances in high-performance computing require new ways to represent large-scale scientific data to support data storage, data transfers, and data analysis within scientific workflows. Multivariate functional approximation (MFA) has recently emerged as a new continuous meshless representation that approximates raw discrete data with a set of piecewise smooth functions. An MFA model of data thus offers a compact representation and supports high-order evaluation of values and derivatives anywhere in the domain. In this paper, we present CPE-MFA, the first critical point extraction framework designed for MFA models of large-scale, high-dimensional data. CPE-MFA extracts critical points directly from an MFA model without the need for discretization or resampling. This is the first step toward enabling continuous implicit models such as MFA to support topological data analysis at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13193 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Critical Point Extraction from Multivariate Functional Approximation Ma, Guanqun Lenz, David Peterka, Tom Guo, Hanqi Wang, Bei Computational Geometry Advances in high-performance computing require new ways to represent large-scale scientific data to support data storage, data transfers, and data analysis within scientific workflows. Multivariate functional approximation (MFA) has recently emerged as a new continuous meshless representation that approximates raw discrete data with a set of piecewise smooth functions. An MFA model of data thus offers a compact representation and supports high-order evaluation of values and derivatives anywhere in the domain. In this paper, we present CPE-MFA, the first critical point extraction framework designed for MFA models of large-scale, high-dimensional data. CPE-MFA extracts critical points directly from an MFA model without the need for discretization or resampling. This is the first step toward enabling continuous implicit models such as MFA to support topological data analysis at scale. |
| title | Critical Point Extraction from Multivariate Functional Approximation |
| topic | Computational Geometry |
| url | https://arxiv.org/abs/2408.13193 |