Diaz De La O, Francisco;
Hristov, PO;
(2023)
Subset simulation for probabilistic computer models.
Applied Mathematical Modelling
, 120
pp. 769-785.
10.1016/j.apm.2023.03.041.
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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.
| Type: | Article |
|---|---|
| Title: | Subset simulation for probabilistic computer models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.apm.2023.03.041 |
| Publisher version: | https://doi.org/10.1016/j.apm.2023.03.041 |
| Language: | English |
| Additional information: | © 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
| Keywords: | partially-converged simulations, probabilistic numerics, subset simulation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics > Clinical Operational Research Unit |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10168825 |
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