Collaborative and Adaptive Bayesian Optimization for bounding variances and probabilities under hybrid uncertainties

Hong, Fangqi, Wei, Pengfei, Song, Jingwen, Valdebenito, Marcos A, Faes, Matthias GR and Beer, Michael ORCID: 0000-0002-0611-0345 (2023) Collaborative and Adaptive Bayesian Optimization for bounding variances and probabilities under hybrid uncertainties. Computer Methods in Applied Mechanics and Engineering, 417. p. 116410.

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Abstract

Uncertainty quantification (UQ) has been widely recognized as of vital importance for reliability-oriented analysis and design of engineering structures, and three groups of mathematical models, i.e., the probability models, the imprecise probability models and the non-probabilistic models, have been developed for characterizing uncertainties of different forms. The propagation of these three groups of models through expensive-to-evaluate simulators to quantify the uncertainties of outputs is then one of the core, yet highly challenging task in reliability engineering, as it involves a demanding double-loop numerical dilemma. For addressing this issue, the Collaborative and Adaptive Bayesian Optimization (CABO) has been developed in our previous work, but it only applies to imprecise probability models and is only capable of bounding the output expectation. We present a substantial improvement of CABO to incorporate all three categories of uncertainty models and to bound arbitrary probabilistic measures such as output variance and failure probability. The algorithm is based on a collaborative active learning mechanism, that is, jointly performing Bayesian optimization in the epistemic uncertainty subspace and Bayesian cubature in the aleatory uncertainty subspace, thus allowing to adaptively produce training samples in the joint uncertainty space. An efficient conditional Gaussian process simulation algorithm is embedded in CABO for acquiring training points and Bayesian inference in both uncertain subspaces. Benchmark studies show that CABO exhibits a remarkable performance in terms of numerical efficiency, accuracy, and global convergence.

Item Type:	Article
Uncontrolled Keywords:	Uncertainty quantification, Imprecise probabilities, Non-probabilistic model, Bayesian optimization, Machine learning, Interval analysis
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	18 Sep 2023 10:59
Last Modified:	27 Oct 2023 10:00
DOI:	10.1016/j.cma.2023.116410
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3172853