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Multilevel Monte Carlo applied for uncertainty quantification in stochastic multiscale systems
Author(s) -
Kimaev Grigoriy,
Chaffart Donovan,
RicardezSandoval Luis A.
Publication year - 2020
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16262
Subject(s) - monte carlo method , polynomial chaos , uncertainty quantification , sampling (signal processing) , observable , mathematical optimization , projection (relational algebra) , heuristic , algorithm , mathematics , computer science , statistical physics , statistics , physics , filter (signal processing) , quantum mechanics , computer vision
Abstract The aim of this study is to evaluate the performance of multilevel Monte Carlo (MLMC) sampling technique for uncertainty quantification in stochastic multiscale systems. Two systems, a chemical vapor deposition chamber and a catalytic flow reactor, subject to multiple parameter uncertainty, were considered. The distributions of the systems' observables were estimated using standard MC sampling and polynomial chaos expansions (PCE), where the coefficients were calculated by nonintrusive spectral projection. The MLMC technique was used to efficiently sample the two systems and accurately estimate the data necessary for constructing the PCE expressions. The results show that the usage of MLMC improved the precision of identification of PCE versus the traditional heuristic approach and lowered the computational cost of uncertainty quantification compared to standard MC.