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 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 supercomputer. A cross- disciplinary atmosphere will be maintained to stimulate the formulation of relevant problems with applicable solutions.
Technical Report 2: Arctic sea ice variability: Model sensitivities and a multidecadal simulation
Technical Report 3: Parameter space of an ocean general circulation model using an isopycnal mixing parameterization
Technical Report 62: Design and Analysis of Computer Experiments When the Output is Highly Correlated Over the Input Space
Technical Report 67: Circuit Optimization Via Sequential Computer Experiments: A Case Study
Principal Investigator(s): Jerome Sacks
Senior Investigator(s): Yong Lim, Ewha Womans University; W.J. Studden, Purdue; William Welch, U. Waterloo