dCG -- differentiable connected geometries for AI-compatible multi-domain optimization and inverse design
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915032090017792 |
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| author | Luce, Alexander Grünbaum, Daniel Marquardt, Florian |
| author_facet | Luce, Alexander Grünbaum, Daniel Marquardt, Florian |
| contents | In the domain of geometry and topology optimization, discovering geometries that optimally satisfy specific problem criteria is a complex challenge in both engineering and scientific research. In this work, we propose a new approach for the creation of multidomain connected geometries that are designed to work with automatic differentiation. We introduce the concept of differentiable Connected Geometries (dCG), discussing its theoretical aspects and illustrating its application through a simple toy examples and a more sophisticated photonic optimization task. Since these geometries are built upon the principles of automatic differentiation, they are compatible with existing deep learning frameworks, a feature we demonstrate via the application examples. This methodology provides a systematic way to approach geometric design and optimization in computational fields involving dependent geometries, potentially improving the efficiency and effectiveness of optimization tasks in scientific and engineering applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05833 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | dCG -- differentiable connected geometries for AI-compatible multi-domain optimization and inverse design Luce, Alexander Grünbaum, Daniel Marquardt, Florian Computational Physics In the domain of geometry and topology optimization, discovering geometries that optimally satisfy specific problem criteria is a complex challenge in both engineering and scientific research. In this work, we propose a new approach for the creation of multidomain connected geometries that are designed to work with automatic differentiation. We introduce the concept of differentiable Connected Geometries (dCG), discussing its theoretical aspects and illustrating its application through a simple toy examples and a more sophisticated photonic optimization task. Since these geometries are built upon the principles of automatic differentiation, they are compatible with existing deep learning frameworks, a feature we demonstrate via the application examples. This methodology provides a systematic way to approach geometric design and optimization in computational fields involving dependent geometries, potentially improving the efficiency and effectiveness of optimization tasks in scientific and engineering applications. |
| title | dCG -- differentiable connected geometries for AI-compatible multi-domain optimization and inverse design |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2410.05833 |