Simulating Stresses and Strains in Solid Mechanics Directly from Images Using Convolutional Neural Networks

In this paper, we introduce a new method for predicting how solid materials deform and the stresses they experience directly from images. Instead of using traditional numerical methods that solve complex equations, our approach uses convolutional neural networks (CNNs) to learn the mapping from applied forces to the resulting deformations. We built a dataset that includes both the visual information of the material and the corresponding force and deformation data. Our experiments show that the CNN model can recognize patterns in the images and accurately predict deformations based on the strength and location of the forces. This simple, data-driven method offers a more efficient way to simulate material behavior and could be used for real-time analysis in engineering applications.