SIAM/ASA Journal on Uncertainty Quantification, Volume 8, Issue 2, Page 717-747, January 2020.
Identification of input configurations so as to meet a prespecified output target under a limited experimental budget has been an important task for computer experiments. Such a task often involves the development of response models and design of experimental trials that rely on the models exhibiting continuity and differentiability properties. Motivated by two canonical examples in systems and manufacturing engineering, we propose a strategy for locating the boundary of the response surface in computer experiments, wherein on one side the response is finite, whereas on the other side it is infinite, leveraging ideas from active learning and quasi-Monte Carlo methods. The strategy is illustrated on an example from computer networks engineering and one from precision manufacturing and shown to allocate experimental trials in a fairly effective manner. We conclude by discussing extensions of the proposed strategy to characterize other types of output discontinuity or nondifferentiability in high-cost experiments, including jump discontinuities in the target output response or pathological structures such as kinks and cusps.

中文翻译：

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在计算机实验中定位无限间断

SIAM / ASA不确定性量化期刊，第8卷，第2期，第717-747页，2020年1月。
确定输入配置以便在有限的实验预算下达到预定的输出目标已成为计算机实验的重要任务。此类任务通常涉及响应模型的开发和实验试验的设计，这些模型依赖于表现出连续性和差异性的模型。根据系统和制造工程中的两个典型示例，我们提出了一种在计算机实验中定位响应面边界的策略，其中一方面响应是有限的，而另一方面则是无限的，并利用了主动学习的思想和准蒙特卡罗方法。该策略在计算机网络工程的一个示例中进行了说明，在精密制造中的一个示例中进行了说明，并显示以相当有效的方式分配了实验性试验。最后，我们讨论了所提议策略的扩展，以表征高成本实验中其他类型的输出不连续性或不可区分性，包括目标输出响应中的跳跃不连续性或病理结构（如扭结和尖瓣）。