应用数学和力学2026，Vol.47Issue(8)：990-998,9.DOI:10.21656/1000-0887.460118
基于PSO-LSTM的负Poisson比材料力学性能预测与参数设计
Mechanical Property Prediction and Parameter Design of Auxetic Materials Based on the PSO-LSTM
摘要
Abstract
To address the challenges of mechanical property prediction and structural optimization design for auxetic honeycomb materials,a machine learning approach integrating the particle swarm optimization(PSO)and the long short-term memory(LSTM)networks was proposed.A training dataset consisting of 400 groups of geometric parameters(including straight wall lengths,cell heights,wall thicknesses,and cell angles)and their corresponding mechanical properties(including energy absorption,Young's moduli,and Poisson's ratios)was constructed through finite element simulation.The PSO algorithm was employed to globally optimize the hyperparameters of the LSTM model,to establish a multi-objective mechanical property prediction model.Fur-thermore,an inverse design framework based on the PSO-LSTM was developed,enabling the optimization of geometric parameters driven by target mechanical properties.Experimental results show that,the optimized PSO-LSTM model achieves R2 values of 0.983 4,0.974 6,and 0.970 4 for energy absorption,Young's moduli,and Poisson's ratios,respectively,with mean squared errors(MSE)below 0.001 2.The relative errors of total energy absorption,Young's moduli,and Poisson's ratios for the model obtained through inverse design are 0.432%,1.05%,and 0.327%,respectively.The proposed method provides theoretical support for the intelligent design and engineering application of auxetic materials.关键词
粒子群优化/长短期记忆网络/负Poisson比材料/几何参数设计Key words
particle swarm optimization/long short-term memory network/auxetic material/geometric pa-rameter design分类
通用工业技术引用本文复制引用
胡雨涵,韩芳,颜芝..基于PSO-LSTM的负Poisson比材料力学性能预测与参数设计[J].应用数学和力学,2026,47(8):990-998,9.基金项目
国家自然科学基金青年基金(51108358) （51108358）
