Mathematical Sciences: Statistical Strategies for Complex Computer Models

本研究为我国农业产业结构调整提供了替代统计模型。 通常是复杂计算机模型的确定性输出， 由于它们的大小， 以及运行次数的限制。这些模型基于 在选定的输入上运行的一组试验，以帮助预测 未经试验的输入，优化输出特性， 识别重要的输入因素并调整模型， 物理数据。这些战略将在以下背景下制定： 大量的输入因素。配料将包括 选择输入的方法（统计实验设计）和 通过使用随机过程分析输出数据 确定性输出的模型。超级计算能力将是 需要处理大量的因素，并提取 在一组昂贵的计算机运行中， 模型本身可以在超级计算机上运行。十字架- 纪律气氛将保持，以刺激 提出相关问题并提出适用的解决办法。

This research provides surrogate statistical models for the usually deterministic output of complex computer models which cannot be directly explored in great detail because of their size and limitations on the number of runs. The models are based on a set of runs at selected inputs to aid in predicting the output at untried inputs, optimizing characteristics of the output, identifying important input factors and tuning the model to physical data. The strategies will be devised in the context of large numbers of input factors. The ingredients will include methods to select inputs (statistical experimental design) and to analyze the output data through the use of stochastic process models for deterministic outputs. Supercomputing power will be necessary to treat large numbers of factors and to extract the most out of an expensive-to-collect set of runs from computer models that themselves may run on supercomputers. A cross- disciplinary atmosphere will be maintained to stimulate the formulation of relevant problems with applicable solutions.