TY - JOUR A1 - Zhang, Chao A1 - Wang, Cuixia A1 - Lahmer, Tom A1 - He, Pengfei A1 - Rabczuk, Timon T1 - A dynamic XFEM formulation for crack identification JF - International Journal of Mechanics and Materials in Design N2 - A dynamic XFEM formulation for crack identification KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2016 SP - 427 EP - 448 ER - TY - JOUR A1 - Zhang, Chao A1 - Nanthakumar, S.S. A1 - Lahmer, Tom A1 - Rabczuk, Timon T1 - Multiple cracks identification for piezoelectric structures JF - International Journal of Fracture N2 - Multiple cracks identification for piezoelectric structures KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2017 SP - 1 EP - 19 ER - TY - JOUR A1 - Vu-Bac, N. A1 - Silani, Mohammad A1 - Lahmer, Tom A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - A unified framework for stochastic predictions of Young's modulus of clay/epoxy nanocomposites (PCNs) JF - Computational Materials Science N2 - A unified framework for stochastic predictions of Young's modulus of clay/epoxy nanocomposites (PCNs) KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2015 SP - 520 EP - 535 ER - TY - JOUR A1 - Vu-Bac, N. A1 - Rafiee, Roham A1 - Zhuang, Xiaoying A1 - Lahmer, Tom A1 - Rabczuk, Timon T1 - Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters JF - Composites Part B: Engineering N2 - Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2015 SP - 446 EP - 464 ER - TY - JOUR A1 - Vu-Bac, N. A1 - Lahmer, Tom A1 - Zhuang, Xiaoying A1 - Nguyen-Thoi, T. A1 - Rabczuk, Timon T1 - A software framework for probabilistic sensitivity analysis for computationally expensive models JF - Advances in Engineering Software N2 - A software framework for probabilistic sensitivity analysis for computationally expensive models KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2016 SP - 19 EP - 31 ER - TY - JOUR A1 - Vu-Bac, N. A1 - Lahmer, Tom A1 - Zhang, Yancheng A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs) JF - Composites Part B Engineering N2 - Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs) KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2014 SP - 80 EP - 95 ER - TY - JOUR A1 - Vu-Bac, N. A1 - Lahmer, Tom A1 - Keitel, Holger A1 - Zhao, Jun-Hua A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations JF - Mechanics of Materials N2 - Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2014 SP - 70 EP - 84 ER - TY - CHAP A1 - Tan, Fengjie A1 - Lahmer, Tom A1 - Siddappa, Manju Gyaraganahalll ED - Gürlebeck, Klaus ED - Lahmer, Tom T1 - SECTION OPTIMIZATION AND RELIABILITY ANALYSIS OF ARCH-TYPE DAMS INCLUDING COUPLED MECHANICAL-THERMAL AND HYDRAULIC FIELDS T2 - Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar N2 - From the design experiences of arch dams in the past, it has significant practical value to carry out the shape optimization of arch dams, which can fully make use of material characteristics and reduce the cost of constructions. Suitable variables need to be chosen to formulate the objective function, e.g. to minimize the total volume of the arch dam. Additionally a series of constraints are derived and a reasonable and convenient penalty function has been formed, which can easily enforce the characteristics of constraints and optimal design. For the optimization method, a Genetic Algorithm is adopted to perform a global search. Simultaneously, ANSYS is used to do the mechanical analysis under the coupling of thermal and hydraulic loads. One of the constraints of the newly designed dam is to fulfill requirements on the structural safety. Therefore, a reliability analysis is applied to offer a good decision supporting for matters concerning predictions of both safety and service life of the arch dam. By this, the key factors which would influence the stability and safety of arch dam significantly can be acquired, and supply a good way to take preventive measures to prolong ate the service life of an arch dam and enhances the safety of structure. KW - Angewandte Informatik KW - Angewandte Mathematik KW - Building Information Modeling KW - Computerunterstütztes Verfahren KW - Data, information and knowledge modeling in civil engineering; Function theoretic methods and PDE in engineering sciences; Mathematical methods for (robotics and) computer vision; Numerical modeling in engineering; Optimization in engineering applications Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170314-28212 SN - 1611-4086 ER - TY - INPR A1 - Steiner, Maria A1 - Bourinet, Jean-Marc A1 - Lahmer, Tom T1 - An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression N2 - In the field of engineering, surrogate models are commonly used for approximating the behavior of a physical phenomenon in order to reduce the computational costs. Generally, a surrogate model is created based on a set of training data, where a typical method for the statistical design is the Latin hypercube sampling (LHS). Even though a space filling distribution of the training data is reached, the sampling process takes no information on the underlying behavior of the physical phenomenon into account and new data cannot be sampled in the same distribution if the approximation quality is not sufficient. Therefore, in this study we present a novel adaptive sampling method based on a specific surrogate model, the least-squares support vector regresson. The adaptive sampling method generates training data based on the uncertainty in local prognosis capabilities of the surrogate model - areas of higher uncertainty require more sample data. The approach offers a cost efficient calculation due to the properties of the least-squares support vector regression. The opportunities of the adaptive sampling method are proven in comparison with the LHS on different analytical examples. Furthermore, the adaptive sampling method is applied to the calculation of global sensitivity values according to Sobol, where it shows faster convergence than the LHS method. With the applications in this paper it is shown that the presented adaptive sampling method improves the estimation of global sensitivity values, hence reducing the overall computational costs visibly. KW - Approximation KW - Sensitivitätsanalyse KW - Abtastung KW - Surrogate models KW - Least-squares support vector regression KW - Adaptive sampling method KW - Global sensitivity analysis KW - Sampling Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20181218-38320 N1 - This is the pre-peer reviewed version of the following article: https://www.sciencedirect.com/science/article/pii/S0951832017311808, which has been published in final form at https://doi.org/10.1016/j.ress.2018.11.015. SP - 1 EP - 33 ER - TY - JOUR A1 - Stein, Peter A1 - Lahmer, Tom A1 - Bock, Sebastian T1 - Synthese und Analyse von gekoppelten Modellen im konstruktiven Ingenieurbau BT - Sonderdruck‐DFG Graduiertenkolleg JF - Bautechnik N2 - Synthese und Analyse von gekoppelten Modellen im konstruktiven Ingenieurbau KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2011 SP - 8 EP - 11 ER -