Search Results for author: Richard D. Whalley

Found 3 papers, 2 papers with code

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

1 code implementation5 Feb 2024 Mike Diessner, Kevin J. Wilson, Richard D. Whalley

ENVBO finds solutions for the full domain of the environmental variable that outperforms 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.

Bayesian Optimisation

NUBO: A Transparent Python Package for Bayesian Optimisation

no code implementations11 May 2023 Mike Diessner, Kevin Wilson, Richard D. Whalley

NUBO, short for Newcastle University Bayesian Optimisation, is a Bayesian optimisation framework for the optimisation of expensive-to-evaluate black-box functions, such as physical experiments and computer simulators.

Bayesian Optimisation Gaussian Processes

Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics

1 code implementation19 Jul 2022 Mike Diessner, Joseph O'Connor, Andrew Wynn, Sylvain Laizet, Yu Guan, Kevin Wilson, Richard D. Whalley

To illustrate how these findings can be used to inform a Bayesian optimization setup tailored to a specific problem, two simulations in the area of computational fluid dynamics are optimized, giving evidence that suitable solutions can be found in a small number of evaluations of the objective function for complex, real problems.

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