The execution and analysis of complex experiments are challenged by the vast dimensionality of the underlying parameter spaces. Although an increase in data-acquisition rates should allow broader querying of the parameter space, the complexity of experiments and the subtle dependence of the model function on input parameters remains daunting owing to the sheer number of variables. New strategies for autonomous data acquisition are being developed, with one promising direction being the use of Gaussian process regression (GPR). GPR is a quick, non-parametric and robust approximation and uncertainty quantification method that can be applied directly to autonomous data acquisition. We review GPR-driven autonomous experimentation and illustrate its functionality using real-world examples from large experimental facilities in the USA and France. We introduce the basics of a GPR-driven autonomous loop with a focus on Gaussian processes, and then shift the focus to the infrastructure that needs to be built around GPR to create a closed loop. Finally, the case studies we discuss show that Gaussian-process-based autonomous data acquisition is a widely applicable method that can facilitate the optimal use of instruments and facilities by enabling the efficient acquisition of high-value datasets.

复杂实验的执行和分析受到潜在参数空间的巨大维度的挑战。尽管数据采集率的提高应该允许对参数空间进行更广泛的查询，但由于变量数量庞大，实验的复杂性和模型函数对输入参数的微妙依赖仍然令人生畏。正在开发用于自主数据采集的新策略，其中一个有希望的方向是使用高斯过程回归 (GPR)。GPR 是一种快速、非参数和稳健的近似和不确定性量化方法，可直接应用于自主数据采集。我们回顾了 GPR 驱动的自主实验，并使用来自美国和法国大型实验设施的真实示例来说明其功能。我们介绍了 GPR 驱动的自主回路的基础知识，重点是高斯过程，然后将重点转移到需要围绕 GPR 构建以创建闭环的基础设施。最后，我们讨论的案例研究表明，基于高斯过程的自主数据采集是一种广泛适用的方法，可以通过高效采集高价值数据集来促进仪器和设施的优化使用。