Design and analysis of experiments in large scale computer simulations

拟议的研究包括两个领域。首先是计算机实验的设计和分析。计算机模型被广泛应用于几乎每一个科学和工程领域来模拟物理过程。由于运行代码的计算负担，通常需要构建代码的统计近似值。本建议中的项目涉及审查与构建良好的近似代码有关的各种问题。例如，生态系统健康和土地利用模式涉及大量投入；在这种情况下，有必要了解这些输入如何影响代码的输出，特别是如何校准模型以匹配所研究的特定地理区域。校正后的模型可用于研究e？气候变化对生态系统的影响，也可用于制定有关土地利用和作物的决策。当统计仿真器不能很好地逼近计算机模型的输出时，还可以考虑顺序收集更多的计算机模型运行。此外，考虑到开发物理过程的有效设计。特别是，我们考虑顺序技术，以增加更多的运行到一个实验，以提高对潜在的积极因素的估计。主要的重点是有效的策略，设计用于工业产品改进的析因实验。

Two areas are covered in the proposed research. The first is the design and analysis of computer experiments. Computer models are widely used in almost every area of science and engineering to model physical processes. Due to the computational burden of running the code, it is often necessary to build a statistical approximation of the code. The projects in this proposal involve examining various issues relating to building a good approximation of the code. For example, models for ecosystem health and land use involve a large number of inputs; in such cases it is necessary to understand how these inputs impact the output of the code and in particular how one can calibrate the model to match the particular geographic area under study. The calibrated model can be used to investigate the e?ects of climate change on ecosystems and can also be used to make decisions regarding land use and crops. When the output from the computer model is not well approximated by the statistical emulator, sequential collection of more computer model runs is also considered. In addition, considerations are given to developing efficient designs for physical processes. In particular we consider sequential techniques to add more runs to an experiment to improve estimates of potentially active factors. The main focus is on efficient strategies for the design of factorial experiments used for industrial product improvement.