Topomamba improves material layout prediction for stronger structures
TopoMamba: A Load-Support Relation-Guided Multi-Directional State-Space Model for Topology Optimization
Artificial IntelligenceComputational Engineering, Finance, and ScienceMachine Learning
Summary
Designing strong structures often means figuring out where to place material to support loads efficiently. Existing artificial intelligence methods can predict such layouts but struggle to understand how forces travel through materials, and they can be slow or inaccurate with new designs. The authors developed TopoMamba, which uses physical relationships about how loads are supported inside structures to make better predictions. This new approach leads to more accurate, faster results and can handle designs unlike those seen before.
What this means in practice
- •For structural engineers: Generate optimized material layouts for structural parts with better load handling and faster computation.
- •For mechanical design teams: Improve prediction accuracy of material distribution in load-bearing components to boost product reliability.
Authors
Bin Lou, Yuxuan Cheng, Huaizhi Zong, Junhui Zhang, Bing Xu
Abstract
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.