nubopy 1.2.1

A transparent Python package for Bayesian optimisation

NUBO

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 simulations. It is developed and maintained by the Fluid Dynamics Lab at Newcastle University. NUBO focuses primarily on transparency and user experience to make Bayesian optimisation easily accessible to researchers from all disciplines. Transparency is ensured by clean and comprehensible code, precise references, and thorough documentation. User experience is ensured by a modular and flexible design, easy-to-write syntax, and careful selection of Bayesian optimisation algorithms. NUBO allows you to tailor Bayesian optimisation to your specific problem by writing the optimisation loop yourself using the provided building blocks or using an off-the-shelf algorithm for common problems. Only algorithms and methods that are sufficiently tested and validated to perform well are included in NUBO. This ensures that the package remains compact and does not overwhelm the user with an unnecessary large number of options. The package is written in Python but does not require expert knowledge of Python to optimise your simulations and experiments. NUBO is distributed as an open-source software under the BSD 3-Clause licence.

Thanks for considering NUBO. If you have any questions, comments, or issues feel free to email us at [e-mail]. Any feedback is highly appreciated and will help make NUBO better in the future.

Install NUBO

Install NUBO and all its dependencies directly from the Python Package Index PyPI using the Python package manager pip with the following code. We recommend the use of a virtual environment.S

pip install nubopy

Cite NUBO

If you are using NUBO for your research, please cite as:

M Diessner, KJ Wilson, and RD Whalley. "NUBO: A Transparent Python Package for Bayesian Optimisation," Journal of Statistical Software, vol. 114, no. 1, p. 1-28, 2025, doi: 10.18637/jss.v114.i01.

If you are using Bibtex, please cite as:

@Article{NUBO,
         author = {Mike Diessner and Kevin J. Wilson and Richard D. Whalley},
         title = {{NUBO}: A Transparent {Python} Package for Bayesian Optimization},
         journal = {Journal of Statistical Software},
         year = {2025},
         volume = {114},
         number = {1},
         pages = {1--28},
         doi = {10.18637/jss.v114.i01}
}