@article{UngerTeughelsDeRoeck,
  author    = {Unger, J{\"o}rg F. and Teughels, A. and De Roeck, G.},
  title     = {Damage detection of a prestressed concrete beam using modal strains},
  series = {Journal of Structural Engineering},
  journal   = {Journal of Structural Engineering},
  pages     = {1456 -- 1463},
  abstract  = {Damage detection of a prestressed concrete beam using modal strains},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{UngerTeughelsDeRoeck,
  author    = {Unger, J{\"o}rg F. and Teughels, A. and De Roeck, G.},
  title     = {System identification and damage detection of a prestressed concrete beam},
  series = {Journal of Structural Engineering},
  journal   = {Journal of Structural Engineering},
  pages     = {1691 -- 1698},
  abstract  = {System identification and damage detection of a prestressed concrete beam},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{UngerKoenke,
  author    = {Unger, J{\"o}rg F. and K{\"o}nke, Carsten},
  title     = {Coupling of scales in a multiscale simulation using neural networks},
  series = {Computers \& Structures},
  journal   = {Computers \& Structures},
  abstract  = {Coupling of scales in a multiscale simulation using neural networks},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@inproceedings{UngerKoenke,
  author    = {Unger, J{\"o}rg F. and K{\"o}nke, Carsten},
  title     = {PARAMETER IDENTIFICATION OF MESOSCALE MODELS FROM MACROSCOPIC TESTS USING BAYESIAN NEURAL NETWORKS},
  editor    = {G{\"u}rlebeck, Klaus and K{\"o}nke, Carsten},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2898},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-28984},
  pages     = {5},
  abstract  = {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.},
  subject      = {Angewandte Informatik},
  language  = {en}
}
@article{UngerEckardtKoenke,
  author    = {Unger, J{\"o}rg F. and Eckardt, Stefan and K{\"o}nke, Carsten},
  title     = {Modelling of cohesive crack growth in concrete structures with the extended finite element method},
  series = {Computer Methods in Applied Mechanics and Engineering},
  journal   = {Computer Methods in Applied Mechanics and Engineering},
  pages     = {4087 -- 4100},
  abstract  = {Modelling of cohesive crack growth in concrete structures with the extended finite element method},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{KoenkeEckardtHaefneretal.,
  author    = {K{\"o}nke, Carsten and Eckardt, Stefan and H{\"a}fner, Stefan and Luther, Torsten and Unger, J{\"o}rg F.},
  title     = {Multiscale simulation methods in damage prediction of brittle and ductile materials},
  series = {International Journal for Multiscale Computational Engineering},
  journal   = {International Journal for Multiscale Computational Engineering},
  pages     = {17 -- 36},
  abstract  = {Multiscale simulation methods in damage prediction of brittle and ductile materials},
  subject      = {Angewandte Mathematik},
  language  = {en}
}