Mathematical Sciences: Statistical Strategies for Complex Computer Models

这项研究为复杂计算机模型的通常确定的输出提供了替代统计模型，由于其大小和运行次数的限制，不能直接详细地探索这些模型。这些模型基于选定输入的一组运行，以帮助预测未尝试输入的输出、优化输出的特性、识别重要的输入因素并将模型调整为物理数据。这些战略将在大量投入因素的背景下制定。内容包括选择输入的方法(统计实验设计)和通过使用确定性输出的随机过程模型分析输出数据的方法。超级计算能力将是处理大量因素和从计算机模型(本身可能在超级计算机上运行)收集成本高昂的运行集的最大限度发挥所必需的。将保持跨学科的氛围，以促进相关问题的制定和适用的解决方案。

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 ate 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 t 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 supercomputer. A cross- disciplinary atmosphere will be maintained to stimulate the formulation of relevant problems with applicable solutions.