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Author (up) Dang, C.; Wei, P.F.; Faes, M.G.R.; Valdebenito, M.A.; Beer, M. doi  openurl
  Title Interval uncertainty propagation by a parallel Bayesian global optimization method Type
  Year 2022 Publication Applied Mathematical Modelling Abbreviated Journal Appl. Math. Model.  
  Volume 108 Issue Pages 220-235  
  Keywords Interval uncertainty propagation; Bayesian global optimization; Gaussian process; Infill sampling criterion; Parallel computing  
  Abstract This paper is concerned with approximating the scalar response of a complex computational model subjected to multiple input interval variables. Such task is formulated as finding both the global minimum and maximum of a computationally expensive black-box function over a prescribed hyper-rectangle. On this basis, a novel non-intrusive method, called `triple-engine parallel Bayesian global optimization', is proposed. The method begins by assuming a Gaussian process prior (which can also be interpreted as a surrogate model) over the response function. The main contribution lies in developing a novel infill sampling criterion, i.e., triple-engine pseudo expected improvement strategy, to identify multiple promising points for minimization and/or maximization based on the past observations at each iteration. By doing so, these identified points can be evaluated on the real response function in parallel. Besides, another potential benefit is that both the lower and upper bounds of the model response can be obtained with a single run of the developed method. Four numerical examples with varying complexity are investigated to demonstrate the proposed method against some existing techniques, and results indicate that significant computational savings can be achieved by making full use of prior knowledge and parallel computing.  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0307-904X ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000830573400001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1625  
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