TopoMamba：一种面向拓扑优化的载荷-支撑关系引导的多向状态空间模型

中文摘要 AI 辅助

深度学习已成为预测拓扑优化中高性能材料分布的一种高效替代方法。现有方法难以准确捕捉载荷传递信息，限制了分布外泛化能力，同时其模型架构往往带来高昂的计算成本。为解决这些挑战，本文提出TopoMamba，一种结合载荷-支撑关系引导的多向状态空间模型的拓扑预测框架。将物理场与载荷-支撑关系耦合，能够更有效地建模力学依赖性。一种载荷-支撑关系引导的空间自适应融合机制根据空间条件动态调整多向扫描特征。Mamba与固体各向同性材料惩罚法（SIMP）相结合，以增强结构力学性能，同时保持计算效率。在二维拓扑优化基准上的结果表明，TopoMamba在拓扑预测精度、分布外泛化能力和计算效率方面均优于最先进的模型。所提出的载荷-支撑物理引导框架能够高效优化更复杂的结构系统。

英文摘要

Deep learning has emerged as an efficient alternative for predicting high-performance material distributions in topology optimization. Existing methods struggle to accurately capture load-transfer information, limiting out-of-distribution generalization, while their model architectures often incur high computational costs. To address these challenges, this paper proposes TopoMamba, a topology prediction framework incorporating a load-support relation-guided multi-directional state-space model. Coupling physical fields with load-support relations enables more effective modeling of mechanical dependencies. A load-support relation-guided spatially adaptive fusion mechanism dynamically adjusts multi-directional scan features according to spatial conditions. Mamba is coupled with the solid isotropic material with penalty method to enhance structural mechanical performance while maintaining computational efficiency. Results on two-dimensional topology optimization benchmarks demonstrate that TopoMamba achieves superior topology prediction accuracy, out-of-distribution generalization, and computational efficiency over state-of-the-art models. The proposed load-support physics-guided framework enables efficient optimization of more complex structural systems.

发表机构

• State Key Laboratory of Fluid Power and Mechatronic Systems（流体动力与机电系统国家重点实验室）
• Zhejiang University（浙江大学）

机构由 AI 辅助整理，请以论文原文为准。