Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of each dimension or random samples that are at least a certain distance apart from each other. When an application imposes new constraints, for example, by requiring samples in a non-rectangular domain or the addition of new samples to an existing set, a common solution is to modify the algorithm currently in use, often with less than satisfactory results. As an alternative, we propose the concept of intelligent sampling, where we devise algorithms specifically tailored to meet our sampling needs, either by creating new algorithms or by modifying suitable algorithms from other fields. Surprisingly, both qualitative and quantitative comparisons indicate that some relatively simple algorithms can be easily modified to meet the many sampling requirements of surrogate modeling, hyperparameter optimization, and data analysis; these algorithms outperform their more sophisticated counterparts currently in use, resulting in better use of time and computer resources.

翻译：采样技术广泛应用于实验设计、图像处理及图形学等多个领域。各领域技术需满足特定约束条件，例如各维度范围的均匀覆盖或随机样本间的最小距离。当应用场景引入新约束（如非矩形区域采样需求或向现有样本集添加新样本时），常见解决方案是修改当前算法，但往往效果欠佳。为此，我们提出智能采样的概念：通过创建新算法或改进其他领域的适用算法，设计专门满足采样需求的方案。令人惊讶的是，定量与定性比较表明，部分相对简单的算法经简易调整后，即可满足代理建模、超参数优化及数据分析中的多数采样需求；这些算法性能显著优于当前使用的复杂方法，从而更有效地利用时间与计算资源。