Green Simulation: A Methodology for Reusing the Output of Past Computer Simulation Experiments

计算机模拟实验的标准做法是运行一个实验来回答一个问题，并且只使用实验的输出来回答这个问题。当将来出现的问题不能通过过去的实验结果充分回答时，这些结果根本不能用来回答这些问题。相反，一个新的实验被运行，就好像它是第一个实验运行的模拟模型。该奖项将有助于通过重用旧实验的输出来提高计算机模拟实验的效率。仿真建模是军事、商业、科学和工程应用中的重要工具。计算机模拟实验往往占用稀缺、昂贵的高性能计算设备，并消耗大量的电力。如果这项研究成功，将减少计算机模拟实验所消耗的资源，从而造福社会。该项目包括培养博士生和将研究成果整合到模拟博士课程中的机会。将努力从代表性不足的群体中招收学生；几名女学生是首席研究员研究小组的一员。本研究的目的是提高随机模拟实验的计算效率，在随机模拟实验中，使用相同的模拟模型进行一系列重复实验，但输入不同。本研究的输出将包括有可能通过存储和重用早期实验的输出来提高后期实验效率的算法。所采用的方法包括似然比法、元模型和随机模拟的方差减少技术。这项研究也适用于更广泛的统计和分析，而不仅仅是模拟。它将开发实验设计，考虑到以前实验数据的可用性，确定可以获得哪些额外的数据来回答手头的问题。

The standard practice in computer simulation experiments is to run an experiment to answer a question, and use the experiment's output only to answer that question. When future questions arise that are not answered adequately by output from past experiments, that output is not used at all in answering them. Instead, a new experiment is run, as though it were the first experiment run with that simulation model. This award will help make methods to make computer simulation experiments more efficient by reusing the output of old experiments. Simulation modeling is an important tool in military, business, science, and engineering applications. Computer simulation experiments often occupy scarce, expensive high-performance computing facilities and consume substantial amounts of electricity. If successful, this research will benefit society by reducing the resources consumed by computer simulation experiments. The project includes opportunities to train Ph. D. students and to integrate research findings into Ph. D. courses on simulation. Efforts will be made to recruit students from underrepresented groups; several female students have been part of the principal investigator's research group.The objective of this research is to improve the computational efficiency of stochastic simulation experiments in a setting in which there is a sequence of repeated experiments using the same simulation model with different inputs. Outputs of this research will include algorithms that have the potential to improve the efficiency of later experiments by storing and reusing the output of earlier experiments. The methods to be employed include the likelihood ratio method, metamodeling, and variance reduction techniques for stochastic simulation. This research is also applicable more broadly to statistics and analytics, not just to simulation. It will develop experimental designs that take into account the availability of data from previous experiments, determining what additional data may be acquired to answer the question at hand.