https://github.com/mikediessner/environmental-conditions-bo

Data and code associated with paper "On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions" currently in review.
https://github.com/mikediessner/environmental-conditions-bo

bayesian-optimisation black-box-optimisation gaussian-process machine-learning optimisation research research-paper

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Data and code associated with paper "On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions" currently in review.

• Host: GitHub
• URL: https://github.com/mikediessner/environmental-conditions-bo
• Owner: mikediessner
• License: bsd-3-clause
• Created: 2023-12-19T15:26:57.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-05T14:04:34.000Z (about 1 year ago)
• Last Synced: 2024-06-06T15:05:31.580Z (about 1 year ago)
• Topics: bayesian-optimisation, black-box-optimisation, gaussian-process, machine-learning, optimisation, research, research-paper
• Language: Python
• Homepage:
• Size: 61.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions

Data and code associated with the paper "On the development of a practical

Bayesian optimisation algorithm for expensive experiments and simulations with

changing environmental conditions" currently in review.

**Abstract:** Experiments in engineering are typically conducted in controlled

environments where parameters can be set to any desired value. This assumes

that the same applies in a real-world setting, which is often incorrect as many

experiments are influenced by uncontrollable environmental conditions such as

temperature, humidity and wind speed. When optimising such experiments, the

focus should be finding optimal values conditionally on these uncontrollable

variables. This article extends Bayesian optimisation to the optimisation of

systems in changing environments that include controllable and uncontrollable

parameters. The extension fits a global surrogate model over all controllable

and environmental variables but optimises only the controllable parameters

conditional on measurements of the uncontrollable variables. The method is

validated on two synthetic test functions, and the effects of the noise level,

the number of environmental parameters, the parameter fluctuation, the

variability of the uncontrollable parameters, and the effective domain size are

investigated. ENVBO, the proposed algorithm from this investigation, is applied

to a wind farm simulator with eight controllable and one environmental

parameter. ENVBO finds solutions for the entire domain of the environmental

variable that outperform results from optimisation algorithms that only focus

on a fixed environmental value in all but one case while using a fraction of

their evaluation budget. This makes the proposed approach very sample-efficient

and cost-effective. An off-the-shelf open-source version of ENVBO is available

via the NUBO Python package.

**Cite:**

**Link:**