  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.
