TY  - CHAP
A1  - Unger, Jörg F.
A1  - Könke, Carsten
ED  - Gürlebeck, Klaus
ED  - Könke, Carsten
T1  - PARAMETER IDENTIFICATION OF MESOSCALE MODELS FROM MACROSCOPIC TESTS USING BAYESIAN NEURAL NETWORKS
N2  - In this paper, a parameter identification procedure using Bayesian neural networks is proposed. Based on a training set of numerical simulations, where the material parameters are simulated in a predefined range using Latin Hypercube sampling, a Bayesian neural network, which has been extended to describe the noise of multiple outputs using a full covariance matrix, is trained to approximate the inverse relation from the experiment (displacements, forces etc.) to the material parameters. The method offers not only the possibility to determine the parameters itself, but also the accuracy of the estimate and the correlation between these parameters. As a result, a set of experiments can be designed to calibrate a numerical model.
KW  - Angewandte Informatik
KW  - Angewandte Mathematik
KW  - Architektur <Informatik>
KW  - Computerunterstütztes Verfahren
KW  - Computer Science Models in Engineering; Multiscale and Multiphysical Models; Scientific Computing
Y1  - 2010
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170314-28984
UR  - http://euklid.bauing.uni-weimar.de/ikm2009/paper.html
SN  - 1611-4086
ER  - 
TY  - JOUR
A1  - Könke, Carsten
A1  - Eckardt, Stefan
A1  - Häfner, Stefan
A1  - Luther, Torsten
A1  - Unger, Jörg F.
T1  - Multiscale simulation methods in damage prediction of brittle and ductile materials
JF  - International Journal for Multiscale Computational Engineering
N2  - Multiscale simulation methods in damage prediction of brittle and ductile materials
KW  - Angewandte Mathematik
KW  - Strukturmechanik
Y1  - 2010
SP  - 17
EP  - 36
ER  -