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 feasibility demonstrations of Bayesian optimisation in 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 in important and well-suited application domains. 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) identifying a representative range of benchmarks, providing example code where necessary; b) introducing a new benchmark, LAQN-BO; and c) promoting a wider use of climate change applications among Bayesian optimisation practitioners.

翻译：贝叶斯优化是一种针对黑箱函数进行优化的强大方法，广泛应用于真实函数评估成本高昂且缺乏梯度信息的场景。在气候变化领域，当模拟模型不可用或采样成本过高时，贝叶斯优化能够有效改善诸多优化问题的应对策略。尽管已有若干研究验证了贝叶斯优化在气候相关应用中的可行性，但目前尚缺乏对应用场景与基准测试的统一综述。本文旨在填补这一空白，以推动贝叶斯优化在重要且适配性强的应用领域中的使用。我们识别出四大主要应用方向：材料发现、风电场布局、可再生能源最优控制以及环境监测。针对每个方向，我们提供了易于使用、便于评估系统性能且能代表真实世界问题的公开基准测试或数据集。鉴于环境监测领域缺乏合适的基准测试，我们基于空气污染数据提出了LAQN-BO基准测试。本文的贡献包括：a) 识别具有代表性的基准测试范围，并在必要时提供示例代码；b) 引入新基准测试LAQN-BO；c) 促进贝叶斯优化从业者更广泛地关注气候变化应用。