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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.20189 |
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| _version_ | 1866909815822876672 |
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| author | Hammond, Alec M. Oskooi, Ardavan Hammond, Ian M. Chen, Mo Ralph, Stephen E. Johnson, Steven G. |
| author_facet | Hammond, Alec M. Oskooi, Ardavan Hammond, Ian M. Chen, Mo Ralph, Stephen E. Johnson, Steven G. |
| contents | We introduce a new "subpixel-smoothed projection" (SSP) formulation for differentiable binarization in topology optimization (TopOpt) as a drop-in replacement for previous projection schemes, which suffer from near-non-differentiability and slow convergence as binarization improves. Our new algorithm overcomes these limitations by depending on both the underlying filtered design field and its spatial gradient, instead of the filtered design field alone. We can now smoothly transition between density-based TopOpt (in which topology can easily change during optimization) and a level-set method (in which shapes evolve in an almost-everywhere binarized structure). We demonstrate the effectiveness of our method on several photonics inverse-design problems and for a variety of computational methods (finite difference, Fourier-modal, and finite-element methods). SSP exhibits both faster convergence and greater simplicity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20189 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unifying and accelerating level-set and density-based topology optimization by subpixel-smoothed projection Hammond, Alec M. Oskooi, Ardavan Hammond, Ian M. Chen, Mo Ralph, Stephen E. Johnson, Steven G. Optics We introduce a new "subpixel-smoothed projection" (SSP) formulation for differentiable binarization in topology optimization (TopOpt) as a drop-in replacement for previous projection schemes, which suffer from near-non-differentiability and slow convergence as binarization improves. Our new algorithm overcomes these limitations by depending on both the underlying filtered design field and its spatial gradient, instead of the filtered design field alone. We can now smoothly transition between density-based TopOpt (in which topology can easily change during optimization) and a level-set method (in which shapes evolve in an almost-everywhere binarized structure). We demonstrate the effectiveness of our method on several photonics inverse-design problems and for a variety of computational methods (finite difference, Fourier-modal, and finite-element methods). SSP exhibits both faster convergence and greater simplicity. |
| title | Unifying and accelerating level-set and density-based topology optimization by subpixel-smoothed projection |
| topic | Optics |
| url | https://arxiv.org/abs/2503.20189 |