@unpublished{SteinerBourinetLahmer,
  author    = {Steiner, Maria and Bourinet, Jean-Marc and Lahmer, Tom},
  title     = {An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression},
  doi       = {10.25643/BAUHAUS-UNIVERSITAET.3832},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181218-38320},
  pages     = {1 -- 33},
  abstract  = {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.},
  subject      = {Approximation},
  language  = {en}
}
@inproceedings{HartmannSmarslyLahmer,
  author    = {Hartmann, Veronika and Smarsly, Kay and Lahmer, Tom},
  title     = {ROBUST SCHEDULING IN CONSTRUCTION ENGINEERING},
  series = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar},
  booktitle = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar},
  editor    = {G{\"u}rlebeck, Klaus and Lahmer, Tom},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2799},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-27994},
  pages     = {5},
  abstract  = {In construction engineering, a schedule's input data, which is usually not exactly known in the planning phase, is considered deterministic when generating the schedule. As a result, construction schedules become unreliable and deadlines are often not met. While the optimization of construction schedules with respect to costs and makespan has been a matter of research in the past decades, the optimization of the robustness of construction schedules has received little attention. In this paper, the effects of uncertainties inherent to the input data of construction schedules are discussed. Possibilities are investigated to improve the reliability of construction schedules by considering alternative processes for certain tasks and by identifying the combination of processes generating the most robust schedule with respect to the makespan of a construction project.},
  subject      = {Angewandte Informatik},
  language  = {en}
}