@article{VuBacRafieeZhuangetal.,
  author    = {Vu-Bac, N. and Rafiee, Roham and Zhuang, Xiaoying and Lahmer, Tom and Rabczuk, Timon},
  title     = {Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters},
  series = {Composites Part B: Engineering},
  journal   = {Composites Part B: Engineering},
  pages     = {446 -- 464},
  abstract  = {Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{GhasemiRafieeZhuangetal.,
  author    = {Ghasemi, Hamid and Rafiee, Roham and Zhuang, Xiaoying and Muthu, Jacob and Rabczuk, Timon},
  title     = {Uncertainties propagation in metamodel-based probabilistic optimization of CNT/polymer composite structure using stochastic multi-scale modeling},
  series = {Computational Materials Science},
  journal   = {Computational Materials Science},
  pages     = {295 -- 305},
  abstract  = {Uncertainties propagation in metamodel-based probabilistic optimization of CNT/polymer composite structure using stochastic multi-scale modeling},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{NanthakumarLahmerZhuangetal.,
  author    = {Nanthakumar, S.S. and Lahmer, Tom and Zhuang, Xiaoying and Park, Harold S. and Rabczuk, Timon},
  title     = {Topology optimization of piezoelectric nanostructures},
  series = {Journal of the Mechanics and Physics of Solids},
  journal   = {Journal of the Mechanics and Physics of Solids},
  pages     = {316 -- 335},
  abstract  = {Topology optimization of piezoelectric nanostructures},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{VuBacLahmerZhangetal.,
  author    = {Vu-Bac, N. and Lahmer, Tom and Zhang, Yancheng and Zhuang, Xiaoying and Rabczuk, Timon},
  title     = {Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs)},
  series = {Composites Part B Engineering},
  journal   = {Composites Part B Engineering},
  pages     = {80 -- 95},
  abstract  = {Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs)},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{VuBacLahmerKeiteletal.,
  author    = {Vu-Bac, N. and Lahmer, Tom and Keitel, Holger and Zhao, Jun-Hua and Zhuang, Xiaoying and Rabczuk, Timon},
  title     = {Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations},
  series = {Mechanics of Materials},
  journal   = {Mechanics of Materials},
  pages     = {70 -- 84},
  abstract  = {Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{RabczukGuoZhuangetal.,
  author    = {Rabczuk, Timon and Guo, Hongwei and Zhuang, Xiaoying and Chen, Pengwan and Alajlan, Naif},
  title     = {Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media},
  series = {Engineering with Computers},
  volume    = {2022},
  journal   = {Engineering with Computers},
  publisher = {Springer},
  address   = {London},
  doi       = {10.1007/s00366-021-01586-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220209-45835},
  pages     = {1 -- 26},
  abstract  = {We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM also saves the weights and biases of the most favorable architectures, which is then used in the fine-tuning process. We also employ transfer learning techniques to drastically reduce the computational cost. The presented DCM is then applied to the stochastic analysis of heterogeneous porous material. Therefore, a three dimensional stochastic flow model is built providing a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The performance of the presented NAS based DCM is verified in different dimensions using the method of manufactured solutions. We show that it significantly outperforms finite difference methods in both accuracy and computational cost.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{GhasemiBrighentiZhuangetal.,
  author    = {Ghasemi, Hamid and Brighenti, Roberto and Zhuang, Xiaoying and Muthu, Jacob and Rabczuk, Timon},
  title     = {Sequential reliability based optimization of fiber content and dispersion in fiber reinforced composite by using NURBS finite elements},
  series = {Structural and Multidisciplinary Optimization},
  journal   = {Structural and Multidisciplinary Optimization},
  abstract  = {Sequential reliability based optimization of fiber content and dispersion in fiber reinforced composite by using NURBS finite elements},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{GuoAlajlanZhuangetal.,
  author    = {Guo, Hongwei and Alajlan, Naif and Zhuang, Xiaoying and Rabczuk, Timon},
  title     = {Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials},
  series = {Computational Mechanics},
  volume    = {2023},
  journal   = {Computational Mechanics},
  publisher = {Springer},
  address   = {Berlin},
  doi       = {10.1007/s00466-023-02287-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20230517-63666},
  pages     = {1 -- 12},
  abstract  = {We present a physics-informed deep learning model for the transient heat transfer analysis of three-dimensional functionally graded materials (FGMs) employing a Runge-Kutta discrete time scheme. Firstly, the governing equation, associated boundary conditions and the initial condition for transient heat transfer analysis of FGMs with exponential material variations are presented. Then, the deep collocation method with the Runge-Kutta integration scheme for transient analysis is introduced. The prior physics that helps to generalize the physics-informed deep learning model is introduced by constraining the temperature variable with discrete time schemes and initial/boundary conditions. Further the fitted activation functions suitable for dynamic analysis are presented. Finally, we validate our approach through several numerical examples on FGMs with irregular shapes and a variety of boundary conditions. From numerical experiments, the predicted results with PIDL demonstrate well agreement with analytical solutions and other numerical methods in predicting of both temperature and flux distributions and can be adaptive to transient analysis of FGMs with different shapes, which can be the promising surrogate model in transient dynamic analysis.},
  subject      = {W{\"a}rme{\"u}bergang},
  language  = {en}
}
@article{JiangZhuangRabczuk,
  author    = {Jiang, Jin-Wu and Zhuang, Xiaoying and Rabczuk, Timon},
  title     = {Orientation dependent thermal conductance in single-layer MoS 2},
  series = {Scientific Reports},
  journal   = {Scientific Reports},
  doi       = {10.1038/srep02209},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170418-31417},
  abstract  = {We investigate the thermal conductivity in the armchair and zigzag MoS2 nanoribbons, by combining the non-equilibrium Green's function approach and the first-principles method. A strong orientation dependence is observed in the thermal conductivity. Particularly, the thermal conductivity for the armchair MoS2 nanoribbon is about 673.6 Wm-1 K-1 in the armchair nanoribbon, and 841.1 Wm-1 K-1 in the zigzag nanoribbon at room temperature. By calculating the Caroli transmission, we disclose the underlying mechanism for this strong orientation dependence to be the fewer phonon transport channels in the armchair MoS2 nanoribbon in the frequency range of [150, 200] cm-1. Through the scaling of the phonon dispersion, we further illustrate that the thermal conductivity calculated for the MoS2 nanoribbon is esentially in consistent with the superior thermal conductivity found for graphene.},
  subject      = {Mechanische Eigenschaft},
  language  = {en}
}
@article{GhasemiBrighentiZhuangetal.,
  author    = {Ghasemi, Hamid and Brighenti, Roberto and Zhuang, Xiaoying and Muthu, Jacob and Rabczuk, Timon},
  title     = {Optimum fiber content and distribution in fiber-reinforced solids using a reliability and NURBS based sequential optimization approach},
  series = {Structural and Multidisciplinary Optimization},
  journal   = {Structural and Multidisciplinary Optimization},
  pages     = {99 -- 112},
  abstract  = {Optimum _ber content and distribution in _ber-reinforced solids using a reliability and NURBS based sequential optimization approach},
  subject      = {Angewandte Mathematik},
  language  = {en}
}