Filters reveal emergent structure in computational morphogenesis
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909545792536576 |
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| author | Aliahmadi, Hazhir Sheedy, Aidan van Anders, Greg |
| author_facet | Aliahmadi, Hazhir Sheedy, Aidan van Anders, Greg |
| contents | Revolutionary advances in both manufacturing and computational morphogenesis raise critical questions about design sensitivity. Sensitivity questions are especially critical in contexts, such as topology optimization, that yield structures with emergent morphology. However, analyzing emergent structures via conventional, perturbative techniques can mask larger-scale vulnerabilities that could manifest in essential components. Risks that fail to appear in perturbative sensitivity analyses will only continue to proliferate as topology optimization-driven manufacturing penetrates more deeply into engineering design and consumer products. Here, we introduce Laplace-transform based computational filters that supplement computational morphogenesis with a set of nonperturbative sensitivity analyses. We demonstrate how this approach identifies important elements of a structure even in the absence of knowledge of the ultimate, optimal structure itself. We leverage techniques from molecular dynamics and implement these methods in open-source codes, demonstrating their application to compliance minimization problems in both 2D and 3D. Our implementation extends straightforwardly to topology optimization for other problems and benefits from the strong scaling properties observed in conventional molecular simulation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_16211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Filters reveal emergent structure in computational morphogenesis Aliahmadi, Hazhir Sheedy, Aidan van Anders, Greg Computational Engineering, Finance, and Science Statistical Mechanics Optimization and Control Revolutionary advances in both manufacturing and computational morphogenesis raise critical questions about design sensitivity. Sensitivity questions are especially critical in contexts, such as topology optimization, that yield structures with emergent morphology. However, analyzing emergent structures via conventional, perturbative techniques can mask larger-scale vulnerabilities that could manifest in essential components. Risks that fail to appear in perturbative sensitivity analyses will only continue to proliferate as topology optimization-driven manufacturing penetrates more deeply into engineering design and consumer products. Here, we introduce Laplace-transform based computational filters that supplement computational morphogenesis with a set of nonperturbative sensitivity analyses. We demonstrate how this approach identifies important elements of a structure even in the absence of knowledge of the ultimate, optimal structure itself. We leverage techniques from molecular dynamics and implement these methods in open-source codes, demonstrating their application to compliance minimization problems in both 2D and 3D. Our implementation extends straightforwardly to topology optimization for other problems and benefits from the strong scaling properties observed in conventional molecular simulation. |
| title | Filters reveal emergent structure in computational morphogenesis |
| topic | Computational Engineering, Finance, and Science Statistical Mechanics Optimization and Control |
| url | https://arxiv.org/abs/2503.16211 |