AI enabled learning and development for Finite Element Analysis used in Mechanical structures

Authors: Ganesh Babu Chandrasekaran

DOI: https://doi.org/10.37082/IJIRMPS.v14.i1.232923

Short DOI: https://doi.org/hbnbzs

Country: United States

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Abstract: Finite Element Analysis (FEA) is a foundational tool in mechanical engineering, enabling the prediction of structural behavior under complex loading, thermal, and environmental conditions. As mechanical systems grow in complexity and design cycles shorten, traditional FEA workflows face increasing pressure to deliver faster, more accurate, and more adaptive simulations. Artificial Intelligence (AI) and machine learning (ML) are emerging as transformative technologies that enhance FEA capability, accelerate learning curves, automate model development, and improve decision making in structural engineering. This paper explores the integration of AI into FEA learning and development, highlighting its impact on model generation, mesh optimization, material behavior prediction, simulation acceleration, and engineering education. It also discusses future directions where AI driven FEA will enable autonomous design, real time digital twins, and intelligent structural health monitoring.

Keywords: Artificial Intelligence (AI), Machine Learning (ML), Finite Element Analysis (FEA), Automated Mesh Generation, Feature Recognition, Adaptive Meshing, Material Model Prediction, Stress–Strain Curve Modeling, Surrogate Modeling, Reduced Order Models (ROMs), Physics Informed Neural Networks (PINNs), Solver Acceleration, Boundary Condition Inference, Digital Image Correlation (DIC), Digital Twins.

Paper Id: 232923

Published On: 2026-02-01

Published In: Volume 14, Issue 1, January-February 2026

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