应用数学和力学2026，Vol.47Issue(9)：1107-1116,10.DOI:10.21656/1000-0887.460112
小样本数据驱动与约束融合的多目标拓扑优化方法
A Small Sample Data Driven and Constraint Fusion Multi-Objective Topology Optimization Method
摘要
Abstract
The traditional volume-compliance multi-objective topology optimization methods rely on intensive finite element calculations,often suffering from low computational efficiency and sparse coverage of the Pareto solution set.To address these issues,an efficient multi-objective topology optimization method based on small sample data driving and constraint fusion was proposed.The core of this method is the establishment of a small sample conditional generative adversarial network embedded with physical constraints,which learns from a limited number of finite element calculations to generate a high-precision and comprehensive Pareto optimal so-lution set for multi-objective topology optimization.By means of the small sample data driven techniques,the relationships between volume fractions and multi-objective topological structures were established.The results show that,the proposed method ensures computational accuracy while minimizing reliance on large-scale finite element calculations.Furthermore,the fusion of multiple physical constraints guarantees high precision map-ping between physical fields and topologies,providing an efficient and reliable solution for multi-objective opti-mization design.关键词
多目标拓扑优化/小样本/数据驱动/物理约束/生成对抗网络Key words
multi-objective topology optimization/small sample data/data driven/physical constraint/gener-ative adversarial network分类
通用工业技术引用本文复制引用
应佳桓,周焕林,孟增..小样本数据驱动与约束融合的多目标拓扑优化方法[J].应用数学和力学,2026,47(9):1107-1116,10.基金项目
国家自然科学基金(12572224 （）
12372195) （）
