@article{JiangWangRabczuk,
  author    = {Jiang, Jin-Wu and Wang, Bing-Shen and Rabczuk, Timon},
  title     = {Why twisting angles are diverse in graphene Moir'e patterns?},
  series = {Journal of Applied Physics},
  journal   = {Journal of Applied Physics},
  abstract  = {Why twisting angles are diverse in graphene Moir'e patterns?},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{FaizollahzadehArdabiliNajafiAlizamiretal.,
  author    = {Faizollahzadeh Ardabili, Sina and Najafi, Bahman and Alizamir, Meysam and Mosavi, Amir and Shamshirband, Shahaboddin and Rabczuk, Timon},
  title     = {Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters},
  series = {Energies},
  journal   = {Energies},
  number    = {11, 2889},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en11112889},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181025-38170},
  pages     = {1 -- 20},
  abstract  = {The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86\% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. \%, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46\% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. \%, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6\% for ethyl ester and 3.1\% for methyl ester, compared with those for the experimental data.},
  subject      = {Biodiesel},
  language  = {en}
}
@article{IlyaniAkmarLahmerBordasetal.,
  author    = {Ilyani Akmar, A.B. and Lahmer, Tom and Bordas, St{\´e}phane Pierre Alain and Beex, L.A.A. and Rabczuk, Timon},
  title     = {Uncertainty quantification of dry woven fabrics: A sensitivity analysis on material properties},
  series = {Composite Structures},
  journal   = {Composite Structures},
  doi       = {10.1016/j.compstruct.2014.04.014},
  pages     = {1 -- 17},
  abstract  = {Uncertainty quantification of dry woven fabrics: A sensitivity analysis on material properties},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@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{ZhangHaoWangetal.,
  author    = {Zhang, Chao and Hao, Xiao-Li and Wang, Cuixia and Wei, Ning and Rabczuk, Timon},
  title     = {Thermal conductivity of graphene nanoribbons under shear deformation: A molecular dynamics simulation},
  series = {Scientific Reports},
  journal   = {Scientific Reports},
  doi       = {10.1038/srep41398},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170428-31718},
  abstract  = {Tensile strain and compress strain can greatly affect the thermal conductivity of graphene nanoribbons (GNRs). However, the effect of GNRs under shear strain, which is also one of the main strain effect, has not been studied systematically yet. In this work, we employ reverse nonequilibrium molecular dynamics (RNEMD) to the systematical study of the thermal conductivity of GNRs (with model size of 4 nm × 15 nm) under the shear strain. Our studies show that the thermal conductivity of GNRs is not sensitive to the shear strain, and the thermal conductivity decreases only 12-16\% before the pristine structure is broken. Furthermore, the phonon frequency and the change of the micro-structure of GNRs, such as band angel and bond length, are analyzed to explore the tendency of thermal conductivity. The results show that the main influence of shear strain is on the in-plane phonon density of states (PDOS), whose G band (higher frequency peaks) moved to the low frequency, thus the thermal conductivity is decreased. The unique thermal properties of GNRs under shear strains suggest their great potentials for graphene nanodevices and great potentials in the thermal managements and thermoelectric applications.},
  subject      = {W{\"a}rmeleitf{\"a}higkeit},
  language  = {en}
}
@article{ZhaoLuRabczuk,
  author    = {Zhao, Jiyun and Lu, Lixin and Rabczuk, Timon},
  title     = {The tensile and shear failure behavior dependence on chain length and temperature in amorphous polymers},
  series = {Computational Materials Science},
  journal   = {Computational Materials Science},
  pages     = {567 -- 572},
  abstract  = {The tensile and shear failure behavior dependence on chain length and temperature in amorphous polymers},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{BenZhaoZhangetal.,
  author    = {Ben, S. and Zhao, Jun-Hua and Zhang, Yancheng and Rabczuk, Timon},
  title     = {The interface strength and debonding for composite structures: review and recent developments},
  series = {Composite Structures},
  journal   = {Composite Structures},
  abstract  = {The interface strength and debonding for composite structures: review and recent developments},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{AreiasRabczukBarbosa,
  author    = {Areias, Pedro and Rabczuk, Timon and Barbosa, J.I.},
  title     = {The extended unsymmetric frontal solution for multiple-point constraints},
  series = {Engineering Computations},
  journal   = {Engineering Computations},
  abstract  = {The extended unsymmetric frontal solution for multiple-point constraints},
  subject      = {Angewandte Mathematik},
  language  = {en}
}