# Bayesian Optimisation Against Climate Change: Applications and Benchmarks

arXiv: https://arxiv.org/abs/2306.04343 · HTML: https://mlsanity.com/paper/2306.04343/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 89 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 29 of 29)

- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Modern Bayesian Experimental Design](https://mlsanity.com/paper/2302.14545/) (2023)
- [Bayesian optimization for materials design](https://mlsanity.com/paper/1506.01349/) (2015)
- [Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation](https://mlsanity.com/paper/2002.08129/) (2020)
- [Joint Entropy Search for Maximally-Informed Bayesian Optimization](https://mlsanity.com/paper/2206.04771/) (2022)
- [Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces](https://mlsanity.com/paper/2304.11468/) (2023)
- [Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation](https://mlsanity.com/paper/1911.12809/) (2019)
- [Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains](https://mlsanity.com/paper/2106.01309/) (2021)
- [Joint Entropy Search for Multi-objective Bayesian Optimization](https://mlsanity.com/paper/2210.02905/) (2022)
- [Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization](https://mlsanity.com/paper/2210.01628/) (2022)
- [Local Bayesian optimization via maximizing probability of descent](https://mlsanity.com/paper/2210.11662/) (2022)
- [Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning](https://mlsanity.com/paper/1906.12086/) (2019)
- [Automatic Termination for Hyperparameter Optimization](https://mlsanity.com/paper/2104.08166/) (2021)
- [Bayesian Optimized Monte Carlo Planning](https://mlsanity.com/paper/2010.03597/) (2020)
- [SnAKe: Bayesian Optimization with Pathwise Exploration](https://mlsanity.com/paper/2202.00060/) (2022)
- [Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces](https://mlsanity.com/paper/2207.00879/) (2022)
- [EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions](https://mlsanity.com/paper/2106.04618/) (2021)
- [Batch Bayesian optimisation via density-ratio estimation with guarantees](https://mlsanity.com/paper/2209.10715/) (2022)
- [Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs](https://mlsanity.com/paper/1703.04211/) (2017)
- [Movement Penalized Bayesian Optimization with Application to Wind Energy Systems](https://mlsanity.com/paper/2210.08087/) (2022)
- [AutoMat: Accelerated Computational Electrochemical systems Discovery](https://mlsanity.com/paper/2011.04426/) (2020)
- [Bayesian Optimisation for Active Monitoring of Air Pollution](https://mlsanity.com/paper/2202.07595/) (2022)
- [Active Collaborative Sensing for Energy Breakdown](https://mlsanity.com/paper/1909.00525/) (2019)
- [Optimising Placement of Pollution Sensors in Windy Environments](https://mlsanity.com/paper/2012.10770/) (2020)
- [Designing Optimal Behavioral Experiments Using Machine Learning](https://mlsanity.com/paper/2305.07721/) (2023)
- [Active Localization of Gas Leaks using Fluid Simulation](https://mlsanity.com/paper/1901.09608/) (2019)
- [Joint Optimization of Wind Farm Layout Considering Optimal Control](https://mlsanity.com/paper/2107.11620/) (2021)
- [Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation](https://mlsanity.com/paper/2203.17065/) (2022)

## Built on by (most cited first, 3 of 3)

- [Transition Constrained Bayesian Optimization via Markov Decision Processes](https://mlsanity.com/paper/2402.08406/) (2024)
- [Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport](https://mlsanity.com/paper/2603.06278/) (2026)
- [Optimal Transport-based Permutation-Invariant Bayesian Optimization of Offshore Wind Farm Layouts](https://mlsanity.com/paper/2606.00009/) (2026)

## Abstract (arXiv)

Bayesian optimisation is a powerful method for optimising black-box functions, popular in settings where the true function is expensive to evaluate and no gradient information is available. Bayesian optimisation can improve responses to many optimisation problems within climate change for which simulator models are unavailable or expensive to sample from. While there have been several demonstrations of climate-related applications, there has been no unifying review of applications and benchmarks. We provide such a review here, to encourage the use of Bayesian optimisation for important and well-suited applications. We identify four main application domains: material discovery, wind farm layout, optimal renewable control and environmental monitoring. For each domain we identify a public benchmark or data set that is easy to use and evaluate systems against, while being representative of real-world problems. Due to the lack of a suitable benchmark for environmental monitoring, we propose LAQN-BO, based on air pollution data. Our contributions are: a) summarising Bayesian optimisation applications related to climate change; b) identifying a representative range of benchmarks, providing example code where necessary; and c) introducing a new benchmark, LAQN-BO.