• Home
  • Search
  • AI-Enhanced Finite Element Method (FEM) for Structural Analysis
  • Cite Icon1
  • https://doi.org/10.52783/jes.8946Copy DOI Icon

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

Show More
  • Abstract
  • Literature Map
  • References
  • Citations
  • Similar Papers
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.

Similar Papers
  • Research Article
  • Citations1

Accurate Dynamic Responses Analysis of Orthotropic Steel Decks Using a Novel Multiscale Time-Varying Boundary Approximation Method

  • Aug 01, 2017
  • International Journal of Structural Stability and Dynamics
  • Yuan Tian +3
  • Research Article
  • Citations7

Temperature field analysis of liquid stratification for LNG tank based on orthogonal ridgelet finite element method

  • Apr 09, 2016
  • Journal of Thermal Analysis and Calorimetry
  • Bin Zhao +5
  • Research Article
  • Citations1

ROPE ELEMENTS WITH MOVING NODES IN ROPE-PULLEY SYSTEMS 1)

  • Dec 26, 2019
  • Chinese Journal of Theoretical and Applied Mechanics
  • Qi Zhaohui +2
  • PDF
  • Research Article
  • Citations3

The Partitioned Mixed Model of Finite Element Method and Interface Stress Element Method with Arbitrary Shape of Discrete Block Element

  • Jan 01, 2013
  • Mathematical Problems in Engineering
  • Zhang Qing +2
  • Research Article
  • Citations16

The boundary element method for elasticity problems with concentrated loads based on displacement singular elements

  • Dec 08, 2018
  • Engineering Analysis with Boundary Elements
  • Wei Zhou +4
  • Research Article
  • Citations12

Vademecum‐based GFEM (V‐GFEM): optimal enrichment for transient problems

  • Mar 10, 2016
  • International Journal for Numerical Methods in Engineering
  • Diego Canales +7
  • Research Article
  • Citations5

The Cooling Rule of the Crude Oil in a Storage Tank Based on a New Finite Element Method

  • Oct 18, 2013
  • Petroleum Science and Technology
  • B Zhao
  • Research Article
  • Citations3

An adjustable degrees-of-freedom numerical method for computing the temperature distribution of electrical devices

  • Jun 01, 2019
  • Electrical Engineering
  • Xinsheng Yang +7
  • Research Article
  • Citations2

Extension of Time-Domain Finite Element Method to Nonlinear Frequency-Sweeping Problems

  • May 01, 2013
  • IEEE Transactions on Magnetics
  • S L Ho +2
  • Research Article
  • Citations114

Machine learning based digital twin for dynamical systems with multiple time-scales

  • Oct 23, 2020
  • Computers & Structures
  • S Chakraborty +1
  • Research Article
  • Citations5

A node-moving algorithm with application to Burgers equation and the Moltz problem

  • Dec 01, 1982
  • Applied Mathematical Modelling
  • A.E Cook +1
  • Research Article
  • Citations40

Solution of atomic Hartree–Fock equations with the P version of the finite element method

  • Dec 01, 1989
  • The Journal of Chemical Physics
  • J R Flores +2
  • Research Article
  • Citations2

Vectorial finite element method for neutron transport solving with preconditioning GMRES acceleration

  • Jan 17, 2024
  • Annals of Nuclear Energy
  • Yahui Wang +2
  • Research Article

Fast finite element electrostatic analysis with domain decomposition method

  • Mar 10, 2023
  • IEICE Electronics Express
  • Siyi Yang +4
  • Research Article
  • Citations2

An integrated optimal design of energy dissipation structures under wind loads considering SSI effect

  • Aug 01, 2019
  • Wind and Structures
  • Xuefei Zhao +2
Cactus Communications logo

Copyright 2026 Cactus Communications. All rights reserved.