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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2605.20816 |
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| _version_ | 1866911700825931776 |
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| author | Bourne, David P. Buze, Maciej Gallouët, Thomas Mérigot, Quentin |
| author_facet | Bourne, David P. Buze, Maciej Gallouët, Thomas Mérigot, Quentin |
| contents | We formulate a framework of polynomial diagrams, which are a generalisation of power diagrams (PDs) and anisotropic power diagrams (APDs) allowing for boundaries between cells to be algebraic curves of a prescribed degree. We show that they arise naturally from rephrasing PDs (APDs) as first-degree (second-degree) instances of linear parametrised minimisation diagrams. We also develop an efficient GPU-accelerated framework for fitting polynomial diagrams to image data using Legendre polynomials and by maximising a regularised concave objective function adapted from classical logistic regression literature. A largely self-contained analysis of the optimisation algorithm is also provided, including identification of scale and gauge invariances and the limiting objective function as the regularisation parameter vanishes. We apply the algorithm to fit polynomial diagrams to electron backscatter diffraction images of steel. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20816 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Polynomial diagrams for microstructure modelling Bourne, David P. Buze, Maciej Gallouët, Thomas Mérigot, Quentin Optimization and Control Materials Science 65D18, 52C22, 90C25, 65K05, 74N15 We formulate a framework of polynomial diagrams, which are a generalisation of power diagrams (PDs) and anisotropic power diagrams (APDs) allowing for boundaries between cells to be algebraic curves of a prescribed degree. We show that they arise naturally from rephrasing PDs (APDs) as first-degree (second-degree) instances of linear parametrised minimisation diagrams. We also develop an efficient GPU-accelerated framework for fitting polynomial diagrams to image data using Legendre polynomials and by maximising a regularised concave objective function adapted from classical logistic regression literature. A largely self-contained analysis of the optimisation algorithm is also provided, including identification of scale and gauge invariances and the limiting objective function as the regularisation parameter vanishes. We apply the algorithm to fit polynomial diagrams to electron backscatter diffraction images of steel. |
| title | Polynomial diagrams for microstructure modelling |
| topic | Optimization and Control Materials Science 65D18, 52C22, 90C25, 65K05, 74N15 |
| url | https://arxiv.org/abs/2605.20816 |