用神经场生成多样结构，无需数据集也能优化设计
Diverse Topology Optimization using Modulated Neural Fields
- 用神经场建模结构，通过显式多样性约束生成多解
- 2D/3D任务中生成解的多样性超越所有已有方法
- 不依赖数据集，保持近最优性能，适合创新设计
拓扑优化（TO）是一类从形式化问题描述中推导出近优几何结构的计算方法。尽管取得成功，传统TO方法仅能生成单一解，限制了替代方案的探索。为此，我们提出基于调制神经场的拓扑优化（TOM）——一种无需数据的方法，通过训练神经网络生成结构合规形状，并借助显式多样性约束探索多样化解。网络采用闭环求解器优化每轮材料分布。训练后模型生成的多种形状均紧密符合设计要求。我们在2D和3D拓扑优化问题上验证了TOM，结果表明其生成解的多样性超过以往任何方法，同时保持近最优性能且无需依赖数据集。该研究为工程与设计提供了新路径，显著提升结构优化中的灵活性与创新性。
原文摘要 · Abstract (English)
Topology optimization (TO) is a family of computational methods that derive near-optimal geometries from formal problem descriptions. Despite their success, established TO methods are limited to generating single solutions, restricting the exploration of alternative designs. To address this limitation, we introduce Topology Optimization using Modulated Neural Fields (TOM) - a data-free method that trains a neural network to generate structurally compliant shapes and explores diverse solutions through an explicit diversity constraint. The network is trained with a solver-in-the-loop, optimizing the material distribution in each iteration. The trained model produces diverse shapes that closely adhere to the design requirements. We validate TOM on 2D and 3D TO problems. Our results show that TOM generates more diverse solutions than any previous method, all while maintaining near-optimality and without relying on a dataset. These findings open new avenues for engineering and design, offering enhanced flexibility and innovation in structural optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。