In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The primary objective is to analyze the impact of different activation functions incorporated within the PINN model on its predictive performance, specifically assessing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The dataset used in the study consists of a varied set of input parameters related to strut diameter, unit cell size, and the corresponding yield stress values. Through this investigation the aim is to understand the effectiveness of the PINN model and the significance of choosing appropriate activation functions for solving complex PDEs in real-world applications. The outcomes suggest that the choice of activation function may have minimal influence on the model's predictive accuracy for this particular problem. The PINN model showcases exceptional generalization capabilities, indicating its capacity to avoid overfitting with the provided dataset. The research underscores the importance of striking a balance between performance and computational efficiency while selecting an activation function for specific real-world applications. These valuable findings contribute to advancing the understanding and potential adoption of PINN as an effective tool for solving challenging PDEs in diverse scientific and engineering domains.

研究探讨了物理信息神经网络（PINN）模型在解决基于输运方程的偏微分方程（PDEs）方面的应用，主要目的是分析在PINN模型中不同激活函数对其预测性能的影响，特别是均方误差（MSE）和平均绝对误差（MAE）的评估。研究使用的数据集包含与支柱直径、单元格尺寸以及相应屈服应力值相关的各种输入参数。研究的结果表明，对于这个特定问题，激活函数的选择可能对模型的预测准确性产生较小的影响。PINN模型展示了出色的泛化能力，表明其能够避免与提供的数据集过度拟合。研究强调了在选择特定实际应用的激活函数时，在性能和计算效率之间取得平衡的重要性。这些宝贵的发现有助于推动PINN作为解决各种科学和工程领域中具有挑战性的PDEs的有效工具的理解和潜在应用。

基于输运方程的物理信息神经网络预测构型材料的屈服强度