Publisher DOI: 10.1002/gamm.201900011
Title: Challenges of order reduction techniques for problems involving polymorphic uncertainty
Language: English
Authors: Pivovarov, Dmytro 
Willner, Kai 
Steinmann, Paul 
Brumme, Stephan 
Müller, Michael 
Srisupattarawanit, Tarin 
Ostermeyer, Georg Peter 
Henning, Carla 
Ricken, Tim 
Kastian, Steffen 
Reese, Stefanie 
Moser, Dieter 
Grasedyck, Lars 
Biehler, Jonas 
Pfaller, Martin 
Wall, Wolfgang 
Kohlsche, Thomas 
Estorff, Otto von 
Gruhlke, Robert 
Eigel, Martin 
Ehre, Max 
Papaioannou, Iason 
Straub, Daniel 
Leyendecker, Sigrid 
Issue Date: May-2019
Source: GAMM Mitteilungen 2 (42): e201900011- (2019-05)
Journal or Series Name: GAMM-Mitteilungen 
Abstract (english): Modeling 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.
URI: http://hdl.handle.net/11420/2963
ISSN: 0936-7195
Institute: Modellierung und Berechnung M-16 
Type: (wissenschaftlicher) Artikel
Funded by: This research was supported by the Deutsche Forschungs-Gemeinschaft (DFG); WI 1181/9-1; STE 544/59-1; OS 166/16-1; RI 1202/6-1; RE 1057/40-1; GR 3179/5-1; WA 1521/23-1; KO 4900/5-1; ES 70/8-1; EI 1050/1-1; STR 1140/6-1; LE 1841/4-1The support of this work by the Deutsche Forschungs-Gemeinschaft (DFG) through the Priority Programme SPP 1886 Polymorphic uncertainty modelling for the numerical design of structures under grants WI 1181/9-1, STE 544/59-1, OS 166/16-1, RI 1202/6-1, RE 1057/40-1, GR 3179/5-1, WA 1521/23-1, KO 4900/5-1, ES 70/8-1, EI 1050/1-1, STR 1140/6-1, and LE 1841/4-1 is gratefully acknowledged.
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