Abstract
Bayesian optimisation is a statistical method that efficiently models and optimises expensive “black-box” functions. This review considers the application of Bayesian optimisation to experimental design, in comparison to existing Design of Experiments (DOE) methods. Solutions are surveyed for a range of core issues in experimental design including: the incorporation of prior knowledge, high dimensional optimisation, constraints, batch evaluation, multiple objectives, multi-fidelity data, and mixed variable types.
Highlights
Experiments are fundamental to scientific and engineering practice
This review has presented an overview of Bayesian optimisation (BO) with application to experimental design
Bayesian Optimisation (BO) was introduced in relation to existing Design of Experiments (DOE) methods such as factorial designs, response surface methodology, and adaptive sampling
Summary
Experiments are fundamental to scientific and engineering practice. A well-designed experiment yields an empirical model of a process, which facilitates understanding and prediction of its behaviour. Multi-start derivative free local optimiser e.g. COBYLA [36], or evolutionary algorithms e.g. ISRES [37], or Lipschitzian methods such as DIRECT [34] None of these are designed to be sample efficient, and all need to evaluate a function many times to perform optimisation. In [25], Bayesian optimisation is applied for high-quality nano-fibre design meeting a required specification of fibre length and diameter within few tens of iterations, greatly accelerating the production process It has been applied in other diverse fields including optimisation of nano-structures for optimal phonon transport [26], optimisation for maximum power point tracking in photovoltaic power plants [27], optimisation for efficient determination of metal oxide grain boundary structures [28], and for optimisation of computer game design to maximise engagement [29].
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