In this paper we propose DKIBO, a Bayesian optimization (BO) algorithm that
accommodates domain knowledge to tune exploration in the search space. Bayesian
optimization has recently emerged as a sample-efficient optimizer for many
intractable scientific problems. While various existing BO frameworks allow the
input of prior beliefs to accelerate the search by narrowing down the space,
incorporating such knowledge is not always straightforward and can often
introduce bias and lead to poor performance. Here we propose a simple approach
to incorporate structural knowledge in the acquisition function by utilizing an
additional deterministic surrogate model to enrich the approximation power of
the Gaussian process. This is suitably chosen according to structural
information of the problem at hand and acts a corrective term towards a
better-informed sampling. We empirically demonstrate the practical utility of
the proposed method by successfully injecting domain knowledge in a materials
design task. We further validate our method's performance on different
experimental settings and ablation analyses.

这篇论文提出了 DKIBO，即一种贝叶斯优化算法，用于调整搜索空间中的探索，它能够融合领域知识。通过利用一个额外的确定性代理模型，我们提出了一种简单的方法来融入获取函数中的结构化知识，以丰富高斯过程的近似能力，以此纠正采样的不足，并在材料设计任务中成功地注入领域知识。我们进一步验证了该方法在不同实验设置和消融分析方面的性能。