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On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions

Lookup NU author(s): Mike Diessner, Professor Kevin Wilson, Dr Richard Whalley

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Abstract

Experiments in engineering are typically conducted in controlled environments where parameters can be set to any desired value. This assumes that the same applies in a real-world setting, which is often incorrect as many experiments are influenced by uncontrollable environmental conditions such as temperature, humidity and wind speed. When optimising such experiments, the focus should be finding optimal values conditionally on these uncontrollable variables. This article extends Bayesian optimisation to the optimisation of systems in changing environments that include controllable and uncontrollable parameters. The extension fits a global surrogate model over all controllable and environmental variables but optimises only the controllable parameters conditional on measurements of the uncontrollable variables. The method is validated on two synthetic test functions, and the effects of the noise level, the number of environmental parameters, the parameter fluctuation, the variability of the uncontrollable parameters, and the effective domain size are investigated. ENVBO, the proposed algorithm from this investigation, is applied to a wind farm simulator with eight controllable and one environmental parameter. ENVBO finds solutions for the entire domain of the environmental variable that outperform results from optimisation algorithms that only focus on a fixed environmental value in all but one case while using a fraction of their evaluation budget. This makes the proposed approach very sample-efficient and cost-effective. An off-the-shelf open-source version of ENVBO is available via the NUBO Python package.


Publication metadata

Author(s): Diessner M, Wilson KJ, Whalley RD

Publication type: Article

Publication status: In Press

Journal: Data Centric Engineering

Year: 2024

Acceptance date: 15/09/2024

ISSN (electronic): 2632-6736

Publisher: Cambridge University Press


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