Collaborative Research: Inference on Expensive, Grey-Box Simulation Models

这笔拨款将允许组织使用随机计算机模拟模型在面临不确定性的情况下做出关键的战略、战术和运营决策，从而促进国家的繁荣和福祉。在供应链物流、运输、医疗保健、国防规划和金融领域，这些决策需要根据对关键绩效指标的估计，从数百万种情景中进行选择。该奖项支持对灵活框架的基础研究，以利用有关模拟系统性能的信息，为迄今无法解决的大规模问题获得更强的推理和更好的决策。学术研究人员和美国领先公司的行业从业者之间的密切合作将产生快速，可靠的算法，这些算法可以针对许多行业和政府机构的各种仿真问题进行定制。所有结果都将以开源软件的形式提供，并以教程和教学材料的形式为研究生工程，数据科学和商业课程提供方法。该研究的动机是现代大规模仿真问题和将仿真模型视为“黑匣子”的最先进通用方法的缺点。这项工作将创建方法，通过提取和利用现代模拟的“灰箱”性质提供的额外结构信息，有效地进行统计推断。具体来说，它将利用这些信息来加强所提供的推理，并为昂贵的随机模拟模型提供可证明的保证。这些方法将考虑多个相互冲突的性能指标，并利用结构信息来驱动模拟运行的顺序实验设计，该奖项反映了美国国家科学基金会的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查进行评估来支持的的搜索.

This grant will advance the national prosperity and welfare by allowing organizations to use stochastic computer simulation models to make critical strategic, tactical and operational decisions in the face of uncertainty. Such decisions in supply chain logistics, transportation, healthcare, defense planning and finance entail choosing from among millions of scenarios based on estimates of key performance indicators. The award supports fundamental research on a flexible framework for exploiting information about simulated system performance to obtain stronger inference and better decisions for hitherto unsolvable large-scale problems. Close collaboration between academic researchers and industrial practitioners at leading U.S. companies will yield rapid, reliable algorithms that can be tailored to a diverse array of simulation problems across many industries and government agencies. All results will be made available as open-source software and the methods communicated in the form of tutorials and instructional materials for graduate engineering, data science and business classes.The research is motivated by modern large-scale simulation problems and the shortcomings of state-of-the-art general-purpose methods that treat a simulation model as a “black box.” The work will create methods that efficiently carry out statistical inference by extracting and exploiting additional structural information available from the “grey-box” nature of modern simulations. Specifically, it will leverage this information to strengthen the delivered inferences and offer certifiable guarantees for expensive stochastic simulation models. The methods will account for multiple, conflicting performance measures and exploit structural information to drive the sequential experiment design of simulation runs, as well as verify and extract the needed structural information from a simulation model to obtain scalable computational methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.