@article{NguyenTuanKoenkeBettziecheetal.,
  author    = {Nguyen-Tuan, Long and K{\"o}nke, Carsten and Bettzieche, Volker and Lahmer, Tom},
  title     = {Numerical modeling and validation for 3D coupled-nonlinear thermo-hydro-mechanical problems in masonry dams},
  series = {Computers \& Structures},
  journal   = {Computers \& Structures},
  pages     = {143 -- 154},
  abstract  = {Numerical modeling and validation for 3D coupled-nonlinear thermo-hydro-mechanical problems in masonry dams},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{LahmerNguyenTuanKoenkeetal.,
  author    = {Lahmer, Tom and Nguyen-Tuan, Long and K{\"o}nke, Carsten and Bettzieche, Volker},
  title     = {Thermo-hydro-mechanische 3-D-Simulation von Staumauern-Modellierung und Validierung},
  series = {WASSERWIRTSCHAFT},
  journal   = {WASSERWIRTSCHAFT},
  pages     = {27 -- 30},
  abstract  = {Thermo-hydro-mechanische 3-D-Simulation von Staumauern-Modellierung und Validierung},
  subject      = {Angewandte Mathematik},
  language  = {de}
}
@article{LuuMartinezRodrigoZabeletal.,
  author    = {Luu, M. and Martinez-Rodrigo, M.D. and Zabel, Volkmar and K{\"o}nke, Carsten},
  title     = {H∞ optimization of fluid viscous dampers for reducing vibrations of high-speed railway bridges},
  series = {Journal of Sound and Vibration},
  journal   = {Journal of Sound and Vibration},
  pages     = {2421 -- 2442},
  abstract  = {H∞ optimization of fluid viscous dampers for reducing vibrations of high-speed railway bridges},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{SchraderKoenke,
  author    = {Schrader, Kai and K{\"o}nke, Carsten},
  title     = {Distributed computing for the nonlinear analysis of multiphase composites},
  series = {Advances in Engineering Software},
  journal   = {Advances in Engineering Software},
  pages     = {20 -- 32},
  abstract  = {Distributed computing for the nonlinear analysis of multiphase composites},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@inproceedings{AhmadZabelKoenke,
  author    = {Ahmad, Sofyan and Zabel, Volkmar and K{\"o}nke, Carsten},
  title     = {WAVELET-BASED INDICATORS FOR RESPONSE SURFACE MODELS IN DAMAGE IDENTIFICATION OF STRUCTURES},
  series = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 04 - 06 2012, Bauhaus-University Weimar},
  booktitle = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 04 - 06 2012, Bauhaus-University Weimar},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2758},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170306-27588},
  pages     = {14},
  abstract  = {In this paper, wavelet energy damage indicator is used in response surface methodology to identify the damage in simulated filler beam railway bridge. The approximate model is addressed to include the operational and surrounding condition in the assessment. The procedure is split into two stages, the training and detecting phase. During training phase, a so-called response surface is built from training data using polynomial regression and radial basis function approximation approaches. The response surface is used to detect the damage in structure during detection phase. The results show that the response surface model is able to detect moderate damage in one of bridge supports while the temperatures and train velocities are varied.},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{SchraderKoenke,
  author    = {Schrader, Kai and K{\"o}nke, Carsten},
  title     = {Hybrid computing models for large-scale heterogeneous 3d microstructures},
  series = {International Journal for Multiscale Computational Engineering},
  journal   = {International Journal for Multiscale Computational Engineering},
  pages     = {365 -- 377},
  abstract  = {Hybrid computing models for large-scale heterogeneous 3d microstructures},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{LutherKoenke,
  author    = {Luther, Torsten and K{\"o}nke, Carsten},
  title     = {Coupled cohesive zone representations from 3D quasicontinuum simulation on aluminum grain boundaries},
  series = {International Journal for Multiscale Computational Engineering},
  journal   = {International Journal for Multiscale Computational Engineering},
  abstract  = {Coupled cohesive zone representations from 3D quasicontinuum simulation on aluminum grain boundaries},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{LahmerKoenkeBettzieche,
  author    = {Lahmer, Tom and K{\"o}nke, Carsten and Bettzieche, Volker},
  title     = {Optimale Positionierung von Messeinrichtungen an Staumauern zur Bauwerks{\"u}berwachung},
  series = {WASSERWIRTSCHAFT},
  journal   = {WASSERWIRTSCHAFT},
  pages     = {16 -- 16},
  abstract  = {Optimale Positionierung von Messeinrichtungen an Staumauern zur Bauwerks{\"u}berwachung},
  subject      = {Angewandte Mathematik},
  language  = {de}
}
@article{LahmerKoenkeBettzieche,
  author    = {Lahmer, Tom and K{\"o}nke, Carsten and Bettzieche, Volker},
  title     = {Optimal positioning of sensors for the monitoring of water dams},
  series = {WASSERWIRTSCHAFT},
  journal   = {WASSERWIRTSCHAFT},
  pages     = {16 -- 19},
  abstract  = {Optimal positioning of sensors for the monitoring of water dams},
  subject      = {Angewandte Mathematik},
  language  = {de}
}
@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}
}