With advances in computing, complex mathematical models implemented in large computer codes have become effective in describing physical processes and real systems. Many industries have made extensive use of computer models to replace physical experiments and seen the benefits in reduced development cycle time and cost. In this proposal a combination of statistical and engineering ideas will be used to develop novel methodologies and techniques for the planning of computer experiments, their modeling and analysis. A Bayesian approach will be developed to integrate data from two sources: high-accuracy but expensive experiments and low-accuracy but cheaper experiments. Since the performance of a system or product can be severely affected by its noise variations, system robustness needs to be considered before performing optimization. New techniques in robust design and optimization will be developed to handle problems with many noise factors and to address multiple performance measures. Specific applications to cellular alloy material design and data center thermal distribution will be considered for illustrating and testing the proposed methodology. Broader applications include manufacturing, material, environmental monitoring, hydrology, and impact dynamics.
