UCL Discovery Stage
UCL home » Library Services » Electronic resources » UCL Discovery Stage

Robust optimisation of computationally expensive models using adaptive multi-fidelity emulation

Ellison, M; DiazDelaO, FA; Ince, NZ; Willetts, M; (2021) Robust optimisation of computationally expensive models using adaptive multi-fidelity emulation. Applied Mathematical Modelling , 100 pp. 92-106. 10.1016/j.apm.2021.07.020. Green open access

[thumbnail of Diaz De La O_1-s2.0-S0307904X21003449-main.pdf]
Preview
Text
Diaz De La O_1-s2.0-S0307904X21003449-main.pdf - Published Version

Download (2MB) | Preview

Abstract

Computationally expensive models are increasingly employed in the design process of engineering products and systems. Robust design in particular aims to obtain designs that exhibit near-optimal performance and low variability under uncertainty. Surrogate models are often employed to imitate the behaviour of expensive computational models. Surrogates are trained from a reduced number of samples of the expensive model. A crucial component of the performance of a surrogate is the quality of the training set. Problems occur when sampling fails to obtain points located in an area of interest and/or where the computational budget only allows for a very limited number of runs of the expensive model. This paper employs a Gaussian process emulation approach to perform efficient single-loop robust optimisation of expensive models. The emulator is enhanced to propagate input uncertainty to the emulator output, allowing single-loop robust optimisation. Further, the emulator is trained with multi-fidelity data obtained via adaptive sampling to maximise the quality of the training set for the given computational budget. An illustrative example is presented to highlight how the method works, before it is applied to two industrial case studies.

Type: Article
Title: Robust optimisation of computationally expensive models using adaptive multi-fidelity emulation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.apm.2021.07.020
Publisher version: https://doi.org/10.1016/j.apm.2021.07.020
Language: English
Additional information: © 2021 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: Robust optimization, Gaussian process emulation, Subset simulation, Multi-fidelity
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-pp.ucl.ac.uk/id/eprint/10134527
Downloads since deposit
3,744Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item