The benefits of sequential design of experiments have long been described for both model-based and space-filling designs. However, in our experience, too few practitioners take advantage of the opportunity afforded by this approach to maximize the learning from their experimentation. By obtaining data sequentially, it is possible to learn from the early stages to inform subsequent data collection, minimize wasted resources, and provide answers for a series of objectives for the overall experiment. This paper provides methods and algorithms to create augmented distance-based space-filling designs, using both uniform and non-uniform space-filling strategies, that can be constructed at each stage based on information learned in earlier stages. We illustrate the methods with several examples that involve different initial data, types of space-filing designs and experimental goals.

中文翻译：

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实验和扩充的顺序设计策略

长期以来，基于模型和空间填充设计都描述了实验顺序设计的好处。然而，根据我们的经验，很少有从业者利用这种方法提供的机会最大限度地从他们的实验中学习。通过顺序获取数据，可以从早期阶段学习为后续数据收集提供信息，最大限度地减少资源浪费，并为整个实验的一系列目标提供答案。本文提供了使用均​​匀和非均匀空间填充策略创建基于增强距离的空间填充设计的方法和算法，这些设计可以根据早期阶段学到的信息在每个阶段构建。我们用几个涉及不同初始数据的例子来说明这些方法，