Subset simulation for probabilistic computer models

Hristov, PO ORCID: 0000-0002-3302-686X and DiazDelaO, FA (2023) Subset simulation for probabilistic computer models. Applied Mathematical Modelling, 120. pp. 769-785.

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Official URL: http://dx.doi.org/10.1016/j.apm.2023.03.041

Abstract

Reliability analysis can be performed efficiently through subset simulation. Through Markov chain Monte Carlo, subset simulation progressively samples from the input domain of a performance function (typically a computer model) to find the failure domain, that is, the set of input configurations that result in an output higher than a prescribed threshold. Recently, a probabilistic framework for numerical analysis was proposed, whereby computation is treated as a statistical inference problem. The framework, called probabilistic numerics, treats the output of a computer code as a random variable. This paper presents a generalisation of subset simulation, which enables reliability analysis for probabilistic numerical models. The advantages and challenges of the method are discussed, and an example with industrial application is presented.

Item Type:	Article
Uncontrolled Keywords:	Reliability analysis, Subset simulation, Probabilistic numerics, Partially-converged simulations
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	25 May 2023 09:17
Last Modified:	14 Jun 2023 19:17
DOI:	10.1016/j.apm.2023.03.041
Open Access URL:	https://doi.org/10.1016/j.apm.2023.03.041
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3170650