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Bayesian optimization for adaptive experimental design: a review

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Version 1 2020-03-16, 08:29
journal contribution
posted on 2024-06-04, 04:11 authored by Stewart GreenhillStewart Greenhill, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, P Vellanki, Svetha VenkateshSvetha Venkatesh
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.

History

Journal

IEEE access

Volume

8

Pagination

13937-13948

Location

Piscataway, N.J.

Open access

  • Yes

ISSN

2169-3536

eISSN

2169-3536

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Institute of Electrical and Electronics Engineers

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