Mechanical Property Prediction and Parameter Design of Auxetic Materials Based on the PSO-LSTM

Hu Yuhan; Han Fang; Yan Zhi

doi:10.21656/1000-0887.460118

Volume 47 Issue 8

Aug. 2026

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Article Navigation > Applied Mathematics and Mechanics > 2026 > 47(8): 990-998

Hu Yuhan, Han Fang, Yan Zhi. Mechanical Property Prediction and Parameter Design of Auxetic Materials Based on the PSO-LSTM[J]. Applied Mathematics and Mechanics, 2026, 47(8): 990-998. doi: 10.21656/1000-0887.460118

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Mechanical Property Prediction and Parameter Design of Auxetic Materials Based on the PSO-LSTM

doi: 10.21656/1000-0887.460118

Hu Yuhan^1
,,
Han Fang^{2
,
,},
Yan Zhi²

1.
School of Mathematics and Systems Science, Wuhan University of Science and Technology, Wuhan 430065, P.R. China
2.
School of Physics and Mechanics, Wuhan University of Science and Technology, Wuhan 430065, P.R. China

Received Date: 2025-06-09
Rev Recd Date: 2025-08-20
Publish Date: 2026-08-01

Abstract

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. Furthermore, 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.
- particle swarm optimization,
- long short-term memory network,
- auxetic material,
- geometric parameter design

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References(18)

References

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