Pivovarov, DmytroDmytroPivovarovWillner, KaiKaiWillnerSteinmann, PaulPaulSteinmannBrumme, StephanStephanBrummeMüller, MichaelMichaelMüllerSrisupattarawanit, TarinTarinSrisupattarawanitOstermeyer, Georg PeterGeorg PeterOstermeyerHenning, CarlaCarlaHenningRicken, TimTimRickenKastian, SteffenSteffenKastianReese, StefanieStefanieReeseMoser, DieterDieterMoserGrasedyck, LarsLarsGrasedyckBiehler, JonasJonasBiehlerPfaller, MartinMartinPfallerWall, WolfgangWolfgangWallKohlsche, ThomasThomasKohlscheEstorff, Otto vonOtto vonEstorffGruhlke, RobertRobertGruhlkeEigel, MartinMartinEigelEhre, MaxMaxEhrePapaioannou, IasonIasonPapaioannouStraub, DanielDanielStraubLeyendecker, SigridSigridLeyendecker2019-07-172019-07-172019-05GAMM Mitteilungen 2 (42): e201900011- (2019-05)http://hdl.handle.net/11420/2963Modeling of mechanical systems with uncertainties is extremely challenging and requires a careful analysis of a huge amount of data. Both, probabilistic modeling and nonprobabilistic modeling require either an extremely large ensemble of samples or the introduction of additional dimensions to the problem, thus, resulting also in an enormous computational cost growth. No matter whether the Monte-Carlo sampling or Smolyak's sparse grids are used, which may theoretically overcome the curse of dimensionality, the system evaluation must be performed at least hundreds of times. This becomes possible only by using reduced order modeling and surrogate modeling. Moreover, special approximation techniques are needed to analyze the input data and to produce a parametric model of the system's uncertainties. In this paper, we describe the main challenges of approximation of uncertain data, order reduction, and surrogate modeling specifically for problems involving polymorphic uncertainty. Thereby some examples are presented to illustrate the challenges and solution methods.en1522-260820192e201900011Journal Article10.1002/gamm.201900011Other