Physical Information Neural Network-Based Mechanical Response Prediction for Sintered Nano-Silver Materials

Deep learning approaches employing physics informed neural networks (PINNs) facilitate the modeling of agents and the analysis of mechanical properties in electronic packaging materials. Specifically, in the study of the mechanical behavior of sintered nano-silver materials, PINNs integrate momentum balance equations along with corresponding material constitutive models. The mechanical physics equations embedded in the PINN ensure that its output follows the laws of physics. The input data to the model are the coordinates of the position within the problem area, while the output data are the corresponding physical field components. Upon the completion of iterative training, the network effectively generates detailed displacement and stress-strain distributions for the material under displacement field loading. By embedding physical principles into the neural network, the accuracy and reliability of predictions for mechanical analysis are significantly enhanced, offering a robust framework for elucidating material behavior under diverse loading conditions.