Bayesian optimization for adaptive experimental design: a review

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.