@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}
}
@inproceedings{UngerMostBucheretal.,
  author    = {Unger, J{\"o}rg F. and Most, Thomas and Bucher, Christian and K{\"o}nke, Carsten},
  title     = {Adaptation of the natural element method for crack growth simulations},
  abstract  = {Adaptation of the natural element method for crack growth simulations},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@inproceedings{UngerKoenke,
  author    = {Unger, J{\"o}rg F. and K{\"o}nke, Carsten},
  title     = {Simulation of concrete using the extended finite element method},
  abstract  = {Simulation of concrete using the extended finite element method},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@inproceedings{UngerKoenke,
  author    = {Unger, J{\"o}rg F. and K{\"o}nke, Carsten},
  title     = {DISCRETE CRACK SIMULATION OF CONCRETE USING THE EXTENDED FINITE ELEMENTMETHOD},
  editor    = {G{\"u}rlebeck, Klaus and K{\"o}nke, Carsten},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  doi       = {10.25643/bauhaus-universitaet.3030},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170327-30303},
  pages     = {12},
  abstract  = {The extended finite element method (XFEM) offers an elegant tool to model material discontinuities and cracks within a regular mesh, so that the element edges do not necessarily coincide with the discontinuities. This allows the modeling of propagating cracks without the requirement to adapt the mesh incrementally. Using a regular mesh offers the advantage, that simple refinement strategies based on the quadtree data structure can be used to refine the mesh in regions, that require a high mesh density. An additional benefit of the XFEM is, that the transmission of cohesive forces through a crack can be modeled in a straightforward way without introducing additional interface elements. Finally different criteria for the determination of the crack propagation angle are investigated and applied to numerical tests of cracked concrete specimens, which are compared with experimental results.},
  subject      = {Architektur <Informatik>},
  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     = {Neural networks as material models within a multiscale approach},
  abstract  = {Neural networks as material models within a multiscale approach},
  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}
}
@inproceedings{UngerEckardtKoenke,
  author    = {Unger, J{\"o}rg F. and Eckardt, Stefan and K{\"o}nke, Carsten},
  title     = {Numerical Models for the simulation of concrete on the mesoscale},
  abstract  = {Numerical Models for the simulation of concrete on the mesoscale},
  subject      = {Angewandte Mathematik},
  language  = {en}
}