https://github.com/mikediessner/nubo
NUBO is a Bayesian optimisation framework for the optimisation of expensive-to-evaluate black-box functions developed by the Fluid Dynamics Lab at Newcastle University.
https://github.com/mikediessner/nubo
bayesian-optimisation black-box-optimisation derivative-free-optimisation gaussian-processes machine-learning optimisation-algorithms
Last synced: 3 months ago
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NUBO is a Bayesian optimisation framework for the optimisation of expensive-to-evaluate black-box functions developed by the Fluid Dynamics Lab at Newcastle University.
- Host: GitHub
- URL: https://github.com/mikediessner/nubo
- Owner: mikediessner
- License: bsd-3-clause
- Created: 2023-02-27T13:09:52.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-11T15:04:35.000Z (about 1 year ago)
- Last Synced: 2025-02-15T07:05:57.653Z (4 months ago)
- Topics: bayesian-optimisation, black-box-optimisation, derivative-free-optimisation, gaussian-processes, machine-learning, optimisation-algorithms
- Language: Python
- Homepage: https://nubopy.com
- Size: 16.7 MB
- Stars: 11
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# 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](https://www.experimental-fluid-dynamics.com) at
[Newcastle University](https://www.ncl.ac.uk). 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](https://www.python.org) 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](https://joinup.ec.europa.eu/licence/bsd-3-clause-new-or-revised-license).
> Thanks for considering NUBO. If you have any questions, comments, or issues
> feel free to email us at [email protected]. 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](https://pypi.org) *PyPI* using the
[Python package manager](https://pip.pypa.io/en/latest/) *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:
Mike Diessner, Kevin J. Wilson, and Richard D. Whalley. "NUBO: A Transparent Python Package for Bayesian Optimisation," arXiv preprint arXiv:2305.06709, 2023.
If you are using Bibtex, please cite as:
```
@article{diessner2023nubo,
title={NUBO: A Transparent Python Package for Bayesian Optimisation},
author={Diessner, Mike and Wilson, Kevin J and Whalley, Richard D},
journal={arXiv preprint arXiv:2305.06709},
year={2023}
}
```