Computationally Efficient Data-Driven Topology Design Independent from High-Infoentropy Initial Dataset
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910046291492864 |
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| author | Yang, Jun Wang, Ziliang Yamasaki, Shintaro |
| author_facet | Yang, Jun Wang, Ziliang Yamasaki, Shintaro |
| contents | Topology optimization (TO) has been widely adopted in engineering design; however, it is prone to being trapped in local optima, particularly in strongly nonlinear problems. Sensitivity-free data-driven topology design (DDTD) offers a promising alternative. Nevertheless, existing DDTD-based methods still depend heavily on prior information or sensitivity-based TO methods for initialization, limiting their generality and independence in engineering applications. In this study, an efficient DDTD-based framework capable of being driven from low information-entropy initial datasets is proposed while improving computational efficiency. To reduce the dependence on high information-entropy initial datasets, a mesh-independent mutation module is introduced as a supplementary source of geometric features, enabling stable exploration under low information-entropy initialization. To alleviate the computational bottleneck in DDTD, where all candidate structures require numerical evaluations, a non-AI-based rapid identification algorithm is developed to efficiently identify potential high-performance structures, thereby significantly reducing the number of expensive high-fidelity simulations. The framework generates material distributions on body-fitted meshes to maintain consistency between numerical simulations and physical manufacturing. A signed distance field-based minimum length constraint is further incorporated to ensure reliable mesh generation. Numerical experiments on strongly nonlinear stress-related problems, together with comparisons with sensitivity-based TO methods, demonstrate the effectiveness of the proposed method. In microfluidic reactor and shell design problems involving non-differentiable constraints, the proposed method successfully addresses scenarios that remain challenging for both sensitivity-based TO and conventional DDTD-based methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_08233 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Computationally Efficient Data-Driven Topology Design Independent from High-Infoentropy Initial Dataset Yang, Jun Wang, Ziliang Yamasaki, Shintaro Computational Physics Topology optimization (TO) has been widely adopted in engineering design; however, it is prone to being trapped in local optima, particularly in strongly nonlinear problems. Sensitivity-free data-driven topology design (DDTD) offers a promising alternative. Nevertheless, existing DDTD-based methods still depend heavily on prior information or sensitivity-based TO methods for initialization, limiting their generality and independence in engineering applications. In this study, an efficient DDTD-based framework capable of being driven from low information-entropy initial datasets is proposed while improving computational efficiency. To reduce the dependence on high information-entropy initial datasets, a mesh-independent mutation module is introduced as a supplementary source of geometric features, enabling stable exploration under low information-entropy initialization. To alleviate the computational bottleneck in DDTD, where all candidate structures require numerical evaluations, a non-AI-based rapid identification algorithm is developed to efficiently identify potential high-performance structures, thereby significantly reducing the number of expensive high-fidelity simulations. The framework generates material distributions on body-fitted meshes to maintain consistency between numerical simulations and physical manufacturing. A signed distance field-based minimum length constraint is further incorporated to ensure reliable mesh generation. Numerical experiments on strongly nonlinear stress-related problems, together with comparisons with sensitivity-based TO methods, demonstrate the effectiveness of the proposed method. In microfluidic reactor and shell design problems involving non-differentiable constraints, the proposed method successfully addresses scenarios that remain challenging for both sensitivity-based TO and conventional DDTD-based methods. |
| title | Computationally Efficient Data-Driven Topology Design Independent from High-Infoentropy Initial Dataset |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2603.08233 |