Coupling of scales in a multiscale simulation using neural networks

Authors:
Jörg F. Unger;Carsten Könke
Affiliations:
Institute of Structural Mechanics, Bauhaus-University Weimar, Marienstrasse 15, D-99423 Weimar, Germany;Institute of Structural Mechanics, Bauhaus-University Weimar, Marienstrasse 15, D-99423 Weimar, Germany
Venue:
Computers and Structures
Year:
2008

Citing 5
Cited 0

Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning

Neural Networks
Neural network design

Neural network design
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Training feedforward networks with the Marquardt algorithm

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multiscale approaches require the coupling of models on different spatial scales. In this paper, a coupling using neural networks is proposed. Based on a set of mesoscale simulations of concrete a system of neural networks is trained to approximate the response. A macroscale constitutive model is obtained by homogenizing the mesoscale response. Special focus is put on the mesh sensitivity, since the mesoscale model includes softening and consequently the width of the localization zone compared to the dimension of the mesoscale model is an additional parameter in the model.