标题： 基于物理信息神经网络架构的多材料结构化晶格结构梁的位移幅值预测 显示英文标题

标题： Advanced Displacement Magnitude Prediction in Multi-Material Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture

摘要： 本文提出了一种创新的方法，用于预测设计的晶格结构中的变形，该方法结合了物理信息神经网络（PINNs）与有限元分析。对基于面心立方结构的晶格梁进行了深入研究，使用五种不同的材料（结构钢、AA6061、AA7075、Ti6Al4V和Inconel 718）在不同的边缘载荷（1000-10000 N）下进行测试。PINN模型通过专有的损失函数将数据驱动的学习与基于物理的限制相结合，从而实现了比线性回归更高的预测精度。PINN优于线性回归，获得了更高的R平方值（0.7923 vs 0.5686）和更低的误差指标（MSE：0.00017417 vs 0.00036187）。在所研究的材料中，AA6061在最大载荷下的位移敏感度最高（0.1014 mm），而Inconel718具有更好的结构稳定性。 显示英文摘要

摘要： 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.