Deep Learning-Augmented Finite Element Framework for Efficient Structural Analysis

Abstract

The finite element method (FEM) is a fundamental structural analysis method, and is frequently restricted to large-scale and high-fidelity problems due to prohibitively high computational costs and time scales. To counter this problem, this paper suggests a deep learning-based augmented finite element model, which can both improve the efficiency of the computations and remain accurate. The suggested method combines a neural network-based surrogate into the standard FEM pipeline, to estimate fields of solutions, as well as speeding up calculations. To achieve the good balance between the data-driven learning and physics-based modeling, a hybrid architecture is created which makes sure that the predictions at the same time are consistent with the governing equations. The structure is crafted to save classical FEM solutions and to dramatically minimize the computational work. As a performance measure, several measures of accuracy are used such as the relative error, root mean squared error (RMSE) and energy norm error, which gives both numerical and physics-based validation. The findings indicate that the suggested approach is able to record considerable cuts in the calculation time but is well-correlated with conventional FEM results. The framework provides a high prediction fidelity with a small loss in accuracy in diverse structural scenarios quantitatively. These results indicate the prospects of integrating deep learning and FEM to create effective, scalable, and trustworthy computational aids to advanced structural analysis applications.