AI-Enhanced Finite Element Method (FEM) for Structural Analysis

Abstract

The Finite Element Method (FEM) has been the foundation of computational structural analysis for a very long time; yet, because to its high computing demand, it has limits when used to applications that are data-intensive, real-time, and large-scale. In response, this research presents a hybrid framework that combines traditional finite element method (FEM) with artificial intelligence (AI), more especially supervised deep learning, in order to improve the effectiveness and scalability of mathematical models of structural systems. The AI-Enhanced FEM framework that has been proposed has been trained on verified FEM datasets, and it has demonstrated the ability to accurately approximate displacement and stress fields across a wide range of structural scenarios. These scenarios include beam deflection, plate bending, and stress concentration around geometrical discontinuities. The model is validated by presenting six comprehensive numerical examples, with the predictions made by AI reaching an accuracy that is within 1–3% of the findings obtained by traditional finite element methods (FEM) and giving up to 500 times quicker calculation. Cross-validation using analytical benchmarks, physics-based feature embedding, and domain-informed neural network design are the three methods that are used to ensure that the methodological rigor is maintained. The talk focusses on the practical benefits as well as the theoretical implications that are associated with hybridizing numerical and data-driven models. This approach is positioned as a revolutionary step towards real-time structural analysis, digital twins, and intelligent infrastructure systems. This study highlights the connection between numerical rigor and machine learning, therefore opening the way for engineering simulations that are interpretable, adaptable, and computationally economical.