Structural Symmetry, Multiplicity, and Differentiability of Eigenfrequencies
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866929687776722944 |
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| author | Sun, Shiyao Khandelwal, Kapil |
| author_facet | Sun, Shiyao Khandelwal, Kapil |
| contents | This work investigates the multiplicity and differentiability of eigenfrequencies in structures with various symmetries. In particular, the study explores how the geometric and design variable symmetries affect the distribution of eigenvalues, distinguishing between simple and multiple eigenvalues in 3-D trusses. Moreover, this article also examines the differentiability of multiple eigenvalues under various symmetry conditions, which is crucial for gradient-based optimization. The results presented in this study show that while full symmetry ensures the differentiability of all eigenvalues, increased symmetry in optimized design, such as accidental symmetry, may lead to non-differentiable eigenvalues. Additionally, the study presents solutions using symmetric functions, demonstrating their effectiveness in ensuring differentiability in scenarios where multiple eigenvalues are non-differentiable. The study also highlights a critical insight into the differentiability criterion of symmetric functions, i.e., the completeness of eigen-clusters, which is necessary to ensure the differentiability of such functions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15357 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structural Symmetry, Multiplicity, and Differentiability of Eigenfrequencies Sun, Shiyao Khandelwal, Kapil Computational Engineering, Finance, and Science Optimization and Control This work investigates the multiplicity and differentiability of eigenfrequencies in structures with various symmetries. In particular, the study explores how the geometric and design variable symmetries affect the distribution of eigenvalues, distinguishing between simple and multiple eigenvalues in 3-D trusses. Moreover, this article also examines the differentiability of multiple eigenvalues under various symmetry conditions, which is crucial for gradient-based optimization. The results presented in this study show that while full symmetry ensures the differentiability of all eigenvalues, increased symmetry in optimized design, such as accidental symmetry, may lead to non-differentiable eigenvalues. Additionally, the study presents solutions using symmetric functions, demonstrating their effectiveness in ensuring differentiability in scenarios where multiple eigenvalues are non-differentiable. The study also highlights a critical insight into the differentiability criterion of symmetric functions, i.e., the completeness of eigen-clusters, which is necessary to ensure the differentiability of such functions. |
| title | Structural Symmetry, Multiplicity, and Differentiability of Eigenfrequencies |
| topic | Computational Engineering, Finance, and Science Optimization and Control |
| url | https://arxiv.org/abs/2501.15357 |