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.
