Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.08233v1</id>\n    <title>Computationally Efficient Data-Driven Topology Design Independent from High-Infoentropy Initial Dataset</title>\n    <updated>2026-03-09T11:03:43Z</updated>\n    <link href='https://arxiv.org/abs/2603.08233v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.08233v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='physics.comp-ph'/>\n    <published>2026-03-09T11:03:43Z</published>\n    <arxiv:primary_category term='physics.comp-ph'/>\n    <author>\n      <name>Jun Yang</name>\n    </author>\n    <author>\n      <name>Ziliang Wang</name>\n    </author>\n    <author>\n      <name>Shintaro Yamasaki</name>\n    </author>\n  </entry>"
}