Deep Learning Meets Classical Physics: Neural Networks for Elastic Plate Analysis

This research bridges physics and artificial intelligence by integrating fundamental governing equations and energy principles into neural network architectures for structural mechanics. By comparing governing equation-based and energy-based approaches, the study demonstrates how physics-informed deep learning enables more accurate and efficient solutions for complex elastic plate problems, with broad applications in aerospace, construction, and manufacturing design optimization.