用物理约束神经网络精准预测多材料晶格梁变形，精度远超传统方法。
Advanced Displacement Magnitude Prediction in Multi-Material Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture
- 融合物理规律与数据学习的神经网络模型，通过自定义损失函数提升预测可靠性。
- 在1000-10000N载荷下，模型R²达0.7923，误差比线性回归降低约52%。
- 发现AA6061最易变形但轻质，Inconel718稳定性最佳，适合高温场景。
本文提出一种结合物理信息神经网络（PINNs）与有限元分析的新方法，用于预测基于立方体心（FCC）的晶格梁结构在不同材料（结构钢、AA6061、AA7075、Ti6Al4V、Inconel 718）和边缘载荷（1000–10000 N）下的变形行为。该模型通过专有损失函数将物理约束嵌入数据驱动学习过程，显著提升预测精度。相比线性回归，其决定系数R²从0.5686提升至0.7923，均方误差（MSE）由0.00036187降至0.00017417。实验表明，AA6061在最大载荷下位移敏感度最高（0.1014 mm），而Inconel718表现出更优的结构稳定性。
原文摘要 · Abstract (English)
This paper proposes an innovative method for predicting deformation in architected lattice structures that combines Physics-Informed Neural Networks (PINNs) with finite element analysis. A thorough study was carried out on FCC-based lattice beams utilizing five different materials (Structural Steel, AA6061, AA7075, Ti6Al4V, and Inconel 718) under varied edge loads (1000-10000 N). The PINN model blends data-driven learning with physics-based limitations via a proprietary loss function, resulting in much higher prediction accuracy than linear regression. PINN outperforms linear regression, achieving greater R-square (0.7923 vs 0.5686) and lower error metrics (MSE: 0.00017417 vs 0.00036187). Among the materials examined, AA6061 had the highest displacement sensitivity (0.1014 mm at maximum load), while Inconel718 had better structural stability.
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