Print Email Facebook Twitter Machine learning based optimization for interval uncertainty propagation Title Machine learning based optimization for interval uncertainty propagation Author Cicirello, A. (TU Delft Mechanics and Physics of Structures) Giunta, F. (TU Delft Mechanics and Physics of Structures) Date 2022 Abstract Two non-intrusive uncertainty propagation approaches are proposed for the performance analysis of engineering systems described by expensive-to-evaluate deterministic computer models with parameters defined as interval variables. These approaches employ a machine learning based optimization strategy, the so-called Bayesian optimization, for evaluating the upper and lower bounds of a generic response variable over the set of possible responses obtained when each interval variable varies independently over its range. The lack of knowledge caused by not evaluating the response function for all the possible combinations of the interval variables is accounted for by developing a probabilistic description of the response variable itself by using a Gaussian Process regression model. An iterative procedure is developed for selecting a small number of simulations to be evaluated for updating this statistical model by using well-established acquisition functions and to assess the response bounds. In both approaches, an initial training dataset is defined. While one approach builds iteratively two distinct training datasets for evaluating separately the upper and lower bounds of the response variable, the other one builds iteratively a single training dataset. Consequently, the two approaches will produce different bound estimates at each iteration. The upper and lower response bounds are expressed as point estimates obtained from the mean function of the posterior distribution. Moreover, a confidence interval on each estimate is provided for effectively communicating to engineers when these estimates are obtained at a combination of the interval variables for which no deterministic simulation has been run. Finally, two metrics are proposed to define conditions for assessing if the predicted bound estimates can be considered satisfactory. The applicability of these two approaches is illustrated with two numerical applications, one focusing on vibration and the other on vibro-acoustics. Subject Bayesian optimizationBounded uncertaintyCommunicating uncertaintyExpensive-to-evaluate deterministic computer modelsGaussian process To reference this document use: http://resolver.tudelft.nl/uuid:da7b481b-1fe1-4217-a3e4-d8e9c2dd6d56 DOI https://doi.org/10.1016/j.ymssp.2021.108619 ISSN 0888-3270 Source Mechanical Systems and Signal Processing, 170 Part of collection Institutional Repository Document type journal article Rights © 2022 A. Cicirello, F. Giunta Files PDF 1_s2.0_S0888327021009493_main.pdf 5.36 MB Close viewer /islandora/object/uuid:da7b481b-1fe1-4217-a3e4-d8e9c2dd6d56/datastream/OBJ/view