Modeling of materials with fading memory using neural networks

DOI10.1002/nme.2518zbMath1183.74366OpenAlexW2014942080MaRDI QIDQ3549786

Markus Oeser, Steffen Freitag

Publication date: 29 March 2010

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nme.2518

zbMATH Keywords

recurrent neural networkmaterial modelingfractional Newton bodyfractional rheological modelmaterial with fading memory

Mathematics Subject Classification ID

Related Items (5)

A second-order numerical scheme for the time-fractional partial differential equations with a time delay ⋮ Viscoelastic constitutive artificial neural networks (vCANNs) -- a framework for data-driven anisotropic nonlinear finite viscoelasticity ⋮ Numerical approach for time-fractional Burgers' equation via a combination of Adams-Moulton and linearized technique ⋮ Computational mechanics enhanced by deep learning ⋮ Prediction of load-displacement curve in a complex structure using artificial neural networks: a study on a long bone

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Time-domain modeling of damping using anelastic displacement fields and fractional calculus
• An algorithm for the numerical solution of differential equations of fractional order
• Using genetic algorithms to select architecture of a feedforward artificial neural network
• Computational algorithms for FE formulations involving fractional operators
• Numerical implementation of a neural network based material model in finite element analysis
• The numerical solution of fractional differential equations: speed versus accuracy

This page was built for publication: Modeling of materials with fading memory using neural networks