A generative learning and graph-based framework for computing field variables in finite element simulations

DOI10.1016/j.cma.2024.117111MaRDI QIDQ6566095

M. Stoffel, Rutwik Gulakala, Vaishnav Bhaskaran

Publication date: 3 July 2024

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

zbMATH Keywords

viscoplasticitygenerative learningmesh-based non-linear regression

Mathematics Subject Classification ID

Plates (74K20) Finite element methods applied to problems in solid mechanics (74S05)

Cites Work

• Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response
• Artificial neural networks in structural dynamics: a new modular radial basis function approach vs. convolutional and feedforward topologies
• An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture
• Stable model order reduction for time-domain exterior vibro-acoustic finite element simulations
• Model order reduction of nonlinear homogenization problems using a Hashin-Shtrikman type finite element method
• Experimental validation of simulated plate deformations caused by shock waves

This page was built for publication: A generative learning and graph-based framework for computing field variables in finite element simulations